Villareal et al. Journal of Neuroinflammation
https://doi.org/10.1186/s12974-024-03196-3

Journal of Neuroinflammation(2024) 21:202

Open Access

RESEARCH

Deterioration of neuroimmune homeostasis
in Alzheimer’s Disease patients who survive
a COVID-19 infection
Jonathan A. B. Villareal1,2, Tim Bathe1,2,6, Gabriela P. Hery3, Jennifer L. Phillips1,2, Wangchen Tsering1,2,4,6 andStefan Prokop1,2,5,6*
Abstract
Growing evidence has implicated systemic infection as a significant risk factor for the development andadvancement of Alzheimer’s disease (AD). With the emergence of SARS-CoV-2 (COVID-19) and the resultant
pandemic, many individuals from the same aging population vulnerable to AD suffered a severe systemic
infection with potentially unidentified long-term consequences for survivors. To study the impact of COVID-19
survival on the brain’s intrinsic immune system in a population also suffering from AD, we profiled post-mortem
brain tissue from patients in the UF Neuromedicine Human Brain and Tissue Bank with a diagnosis of AD whosurvived a COVID-19 infection (COVID-AD) and contrasted our findings with AD patients who did not experiencea COVID-19 infection, including a group of brain donors who passed away before arrival of SARS-CoV-2 in the
United States. We assessed disease-relevant protein pathology and microglial and astrocytic markers by quantitative
immunohistochemistry and supplemented these data with whole tissue gene expression analysis performed on
the NanoString nCounter® platform. COVID-AD patients showed slightly elevated Aβ burden in the entorhinal,
fusiform, and inferior temporal cortices compared to non-COVID-AD patients, while tau pathology burden did
not differ between groups. Analysis of microglia revealed a significant loss of microglial homeostasis as well as
exacerbated microgliosis in COVID-AD patients compared to non-COVID-AD patients in a brain region-dependent
manner. Furthermore, COVID-AD patients showed reduced cortical astrocyte numbers, independent of functional
subtype. Transcriptomic analysis supported these histological findings and, in addition, identified a dysregulationof oligodendrocyte and myelination pathways in the hippocampus of COVID-AD patients. In summary, our data
demonstrate a profound impact of COVID-19 infection on neuroimmune and glial pathways in AD patients
persisting for months post-infection, highlighting the importance of peripheral to central neuroimmune crosstalk inneurodegenerative diseases.
Keywords Microglia, COVID-19, Alzheimer’s disease, Astrocytes, Systemic infection, Neuroinflammation,
Oligodendrocytes*Correspondence:
Stefan Prokop
sprokop@ufl.edu
1
Department of Pathology, Immunology & Laboratory Medicine,
University of Florida, Gainesville, FL 32610, USA
2
Center for Translational Research in Neurodegenerative Disease,
University of Florida, Gainesville, FL 32610, USA

3

Department of Small Animal Clinical Sciences, College of Veterinary
Medicine, University of Florida, Gainesville, FL 32608, USA
4
Department of Neuroscience, University of Florida, Gainesville, FL
32610, USA
5
Fixel Institute for Neurological Diseases, University of Florida, Gainesville,
FL 32608, USA
6
McKnight Brain Institute, University of Florida, Gainesville, FL 32610, USA© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use,sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) andthe source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in thisarticle are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not includedin the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you willneed to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The
Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available
in this article, unless otherwise stated in a credit line to the data.

Villareal et al. Journal of Neuroinflammation(2024) 21:202

Background
Alzheimer’s disease (AD) is a chronic neurodegenerative
disorder that has persisted as one of humanity’s longestlasting challenges for its aging population. Manifesting in
the form of progressive cognitive decline and neurological complications that can eventually lead to death, thebrains of individuals suffering from AD are characterized by a compromised neural environment laden withbeta-amyloid (Aβ) plaques and hyperphosphorylated tau
in the form of neurofibrillary tangles (NFT). Despite thehuman brain existing as a relatively immune-privilegedsite within the body, mounting evidence has suggested
the involvement of the peripheral and central nervous
system (CNS) immunity – as well as the communication
between the two - in the pathogenesis of neurodegenerative proteinopathies [1–6]. While current literature onwhether neuroimmune involvement accelerates or mitigates AD progression is still highly divisive, it has beensuggested that sustained inflammation within the brain
parenchyma could make it more vulnerable to degenerative processes [7]. Emerging research has now shifted
toward understanding the potential role that peripheral
inflammation can play in the development of neurodegenerative diseases such as AD. Systemic infections,defined as infections that are not limited to a single organor region of the body, can act as sources of peripheral
inflammation, with diseases such as influenza, sepsis, andmore recently COVID-19 exhibiting body-wide sequelae[8–10].
COVID-19, caused by the SARS-CoV-2 virus andresponsible for the recent global pandemic, is of particular interest due to its ubiquitous distribution worldwide
and preliminary findings suggesting specific neurological involvement amongst other symptoms [11]. As of
March 2024, more than 774 million confirmed cases of
COVID-19 have been reported since the emergenceof the pathogen, accounting for over 7 million deaths
and equivalent to almost 10% of the world’s population having experienced systemic infection via SARSCoV-2, not including unreported cases [12]. COVID-19
has presented with various neurological sequelae, with
reports of persistent ‘brain fog’ and cognitive impairment months post-infection in the absence of sustained
viral positivity (RNA and protein) in the brain [11, 13,14]. These findings are suggestive of the ability of peripheral systemic infections (i.e. lungs in COVID-19) to communicate chronic damage to the brain in the absence of
direct neuroinvasion and persistence, potentially throughinflammation-mediated pathways. Within the CNS,
microglia act as the main immune-modulating cell type
and the resident macrophages of the parenchyma, withpowerful cytokine-generating and phagocytotic functions [15]. Neuropathological studies have shown thatelderly, non-AD patients who have an active and severe

Page 2 of 27

COVID-19 infection at the time of death present withheightened microgliosis and microglial activation [16].
Similarly, patients with end-stage AD have been observed
to possess dysregulated microglial homeostasis, withaugmented microgliosis and chronically activated phenotypes being shared between COVID-19 and AD [17–20].
Considering the significant overlap between the vulnerable age ranges for these diseases (65 + for both COVID19 and AD) [21, 22], there remains an important gap inknowledge concerning the intersection of these two
diseases and the interactions between their respective
immunomodulatory effects.
To evaluate the potential effects of surviving a systemic infection on AD pathophysiology, we performed
immunohistochemical (IHC) profiling of microglia
from individuals with a post-mortem AD diagnosis whoeither reported a COVID-19 infection during their lives(COVID) or did not (non-COVID). We also extended thisanalysis to astrocytes due to their accessory roles in neuroimmunity [23–26] as well as their role in the formationof the blood-brain barrier (BBB) [27, 28], both of whichhave been implicated to be involved in AD and COVID19 outcomes. Previous studies have identified the choroid
plexus (CP) as a primary interface for communicating
inflammation between the periphery and the CNS during
active COVID-19 infection [14, 29, 30], making CP-adjacent regions such as the hippocampus more susceptible
to immune augmentation. As microglial activation in AD
shows brain region-specific patterns [31], we decided toevaluate these immune interactions across 4 locations in
the brain, taking both AD staging and CP proximity into
account. Additionally, we performed whole tissue transcriptomics using the NanoString nCounter® platform toevaluate gene expression profiles of glial cells in COVIDAD patients and to identify other novel changes potentially associated with co-occurrence of COVID-19 andAD.

Methods and materials
Patient tissue samples and cohort selection

Human post-mortem tissue was obtained from the University of Florida Human Brain and Tissue Bank following
approval by the UF Institutional Review Board. Alzheimer’s Disease neuropathological changes (ADNC) and
AD staging for each patient were evaluated by a boardcertified neuropathologist, and all patients selected hada primary diagnosis of AD with “high” ADNC based on
current NIA-AA guidelines for pathological diagnosis[32, 33] (Table 1). Co-pathology in the form of Lewy bodydisease LBD, Limbic-predominant age-related TDP-43
proteinopathy (LATE), Aging-related tau astrogliopathy (ARTAG), cerebral amyloid angiopathy (CAA) wasclassified according to current guidelines [32–38] andas detailed in previous articles from our group [31, 39]

Villareal et al. Journal of Neuroinflammation(2024) 21:202

Table 1 Cohort group demographics
N
Mean age
Sex M/F
Mean Thal staging
Mean Braak staging
Mean post-mortem interval
Mean post-infection interval

Pre-2020
12
77.8
6/6
4.83
5.25
14.8
-

Non-COVID
12
78.2
4/8
5
5.25
38
-

COVID
11
79.6
5/6
4.82
5.55
36.6
4.55 months(Table 2). AD patients were grouped based on whether
they died prior to SARS-CoV-2 arrival in the UnitedStates (Pre-2020), did not have a medical history of
COVID-19 infection (Non-COVID), or had one or more
self-reported COVID-19 infections that were resolved
or did not result in death (COVID) (Table 1) (Fig. 1D).
The Pre-2020 group was included to assess the possibility of asymptomatic COVID-19 infection (as opposed tosevere and reported infection) in the Non-COVID groupand to compare it to individuals who could not have
had COVID-19 at any point in their lives. Post-infection
interval for COVID patients was calculated as the period
between reported infection and time of death (Tables 1,
and 2).
Immunohistochemistry

Glial and protein IHC profiling was performed on either
5–10 μm thick formalin-fixed paraffin-embedded (FFPE)
sections depending on the targeted protein (Table 3).
Staining was performed as previously described by ourlaboratory [31] and specific pretreatment protocols foreach antibody are summarized below (Table 3). Additionally, sections stained for Olig2 required agitation on anorbital shaker (OHAUS, tilt and speed setting 2) during
overnight primary and secondary antibody incubations
to produce an adequate signal. GFAP and ALDH1L1
stains were performed with ImmPRESS HRP (VectorLabs) of their respective species added to the goat antirabbit/mouse IgG HRP conjugated secondary antibody(Millipore Sigma) at a dilution of 1:1000.
Immunofluorescent double-stain analysis of CD31 and
GFAP was performed on 5 μm thick FFPE sections following a majority of the same steps as described above.
GFAP was developed in the Texas Red channel with astandard, directly conjugated fluorescent secondary antibody, and CD31 in the FITC channel using ImmPRESS
HRP (Vector Labs) in combination with Opal HRP fluorophores (Akoya Biosciences, USA). Sections were
mounted using VECTASHIELD HardSet Antifade
Mounting Medium with DAPI (Vector Labs) and stored
in a dark chamber at 4 °C for long-term storage. Further
detail into the immunofluorescent staining procedurecan be found below (TableS 3, 4 and 5).

Page 3 of 27

Automated cell and protein coverage quantification

Quantification of IHC stains was performed using
QuPath digital analysis [40] as previously performed
and validated within our laboratory [31]. The positive
cell detection tool was utilized for quantifying all glial
stains and positive pixel count was used to quantify thepercent of tissue covered by pathologies and PLP1.The
scripts used for tissue annotation, pathological coveragequantification, and microglial cell count quantification
are available in our laboratory’s previous manuscript [31],and were adjusted for the quantification of astrocytes andoligodendrocytes. Whole-tissue scanning at 20x magnification of fluorescent sections was performed using an
Oympus VS200 (Olympus, Japan), with manually applied
focus points. QuPath was also used for colocalization andproximity analysis of whole-tissue immunofluorescentscans. For colocalization measurements, the pixel classifier/thresholder tool was used to first create annotations
for CD31 followed by the creation of GFAP annotations
within these boundaries. The percentage overlap for theareas of these two annotations was determined in Excel
using the measurement outputs from QuPath. For vessel proximity analysis, a base script provided by image.
sc user Egor Zindy (EP.Zindy) [41] was modified toautomate the creation of 7 concentric rings, each with aradius of 10 μm, around CD31-positive vessels across anentire brain region (i.e. the hippocampus). Within areas
on the tissue where there are less than 70 μm between
adjacent blood vessels, ring annotations will merge as toaccurately quantify the distance from the nearest bloodvessel while taking into account the surrounding ones aswell. QuPath cell detection plugin StarDist [42] was thenused to quantify GFAP-positive astrocytes within each ofthese rings to obtain proximity-based densities of the cell
type.
Frozen tissue RNA isolation and quality analysis

Human mRNA was isolated from fresh frozen frontal
cortex tissue that was kept at -80 °C for long-term storage. One 25–35 mg chunk of tissue was disrupted into afine powder in a pre-chilled mortar and pestle with liquid
nitrogen added to the mortar to prevent thawing of thetissue. The powdered tissue was then transferred to prechilled, RNAse-free tubes and 500 µL of TRIzol reagent(ThermoFisher Scientific, USA) was added. The TRIzol
Reagent user guide provided by ThermoFisher Scientific(https://assets.thermofisher.com/TFS-Assets/LSG/manuals/trizol_reagent.pdf ) was then followed until step 7(“Tissues” starting material, volumes adapted for 500 µL
starting TRIzol). The aqueous phase from step 7 was thenpurified using columns from the Qiagen RNeasy Mini
Kit (Cat. Number 74104) beginning from step 5 of the
protocol. Isolated RNA was eluted twice (first by loading
water onto the column and then by reloading the eluate

Cohort
Group

Covid
Covid
Covid
Covid
Covid
Covid
Covid

Covid
Covid

Covid
Covid
Non-Covid
Non-Covid
Non-Covid
Non-Covid
Non-Covid
Non-Covid
Non-Covid
Non-Covid
Non-Covid
Non-Covid
Non-Covid
Non-Covid
Pre-2020
Pre-2020
Pre-2020
Pre-2020
Pre-2020
Pre-2020
Pre-2020
Pre-2020
Pre-2020
Pre-2020
Pre-2020
Pre-2020

Patient

1
2
3
4
5
6
7

8
9

10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35

AD High
AD High
AD High
AD High
AD High
AD High
AD High
AD High
AD High
AD High
AD High
AD High
AD High
AD High
AD High
AD High
AD High
AD High
AD High
AD High
AD High
AD High
AD High
AD High
AD High
AD High

AD High
AD High

AD High
AD High
AD High
AD High
AD High
AD High
AD High

Neuropathalogical
Diagnosis

f
m
f
f
f
f
f
m
m
m
f
f
f
m
m
f
f
m
f
f
f
m
f
m
m
m

f
f

f
f
m
m
m
f
m

Sex

Table 2 Individual patient demographics

80
91
73
80
80
83
71
80
77
78
88
83
76
69
73
90
82
84
72
78
78
78
86
60
70
83

62
77

79
76
78
83
71
85
94

Age
(Years)

5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
4
5
5
4
5
5
5
5
5

5
5

4
5
5
5
4
5
5

Thal

VI
VI
V
V
V
V
V
V
V
VI
V
V
VI
VI
V
V
V
V
V
V
V
V
V
VI
VI
VI

VI
VI

V
VI
VI
V
V
V
V

Braak

frequent
frequent
frequent
frequent
frequent
frequent
frequent
frequent
frequent
frequent
frequent
frequent
frequent
frequent
frequent
frequent
frequent
frequent
frequent
frequent
frequent
frequent
frequent
frequent
frequent
frequent

frequent
frequent

frequent
frequent
frequent
frequent
frequent
frequent
frequent

CERAD

CAA moderate, Diffuse acute SAH and IVH
CAA mild, LATE NC stage 2, hippocampal sclerosis
CAA moderate
CAA widespread, moderate
CAA moderate
CAA mild
CAA moderate, LBD limbic-transitional, Remote
microinfarct in cerebellum
CAA moderate, LBD amygdala predominant
CAA moderate, LATE stage 2, hippocampal sclerosis,
LBD (amygdala predominant)
CAA severe
CAA severe, LBD diffuse neocortical, LATE stage 1
CAA severe
CAA moderate
CAA mild
CAA mild
CAA moderate, LATE stage 1
CAA severe
CAA moderate, LATE stage 2, hippocampal sclerosis
CAA mild, LBD diffuse neocortical
CAA moderate, LBD amygdala predominant
CAA moderate
None reported
CAA moderate, LBD amygdala predominant
CAA moderate
CAA mild, LATE stage 1
CAA mild, LATE stage 1
CAA moderate
CAA severe
CAA moderate
CAA moderate, LATE stage 2
CAA mild
CAA moderate
CAA severe
CAA moderate
CAA moderate

Co-Pathologies

1
2

5
11

3/4
3/4
3/3
3/3
3/4
3/4
3/3
4/4
3/3

3/3
4/4
3/4
3/4
3/4
3/3
4/4
3/4
3/4
3/4
2/3
3/3
3/3
3/4
3/4
3/3

3/3
3/3

2/3
3/4
4/4
4/4
3/3
3/4
3/3

ApoE

74
62
71
78
91
54
76
80
78
81
73
78
75
76

76
80

50
77
58
68
77
66
80

DV200
(%)

(2024) 21:202

16
12
5
20
20
14
20
8
18

19
14.25
3 days34
5 days
24
20
15
23
22
4 days
9
3.5
18

36
9

Post-morPosttem Interval Infection(Hours)Interval(Months)
39.5
5
11
1
35
13
34
1
46
1
15
8
6 days2Villareal et al. Journal of Neuroinflammation
Page 4 of 27

Villareal et al. Journal of Neuroinflammation(2024) 21:202

Page 5 of 27

Fig. 1 Cohort overview and regional differences in COVID-AD. (A) Four regions of interest (MFG, STG, Hippocampus (Hipp.), and FG/ITG) were analyzed
in the brains of each patient. Regions were selected with respect to a combination of regional differences in AD progression and CP proximity. Microglial
profiling was performed on all four regions and bulk mRNA analysis was performed on the regions nearest and furthest to the CP (hippocampus andmedial frontal gyrus, respectively). (B) For glial profiling, hippocampal sections were segmented by annotation into the four CA subfields (CA4-CA1), thesubiculum, and the entorhinal cortex. The FG/ITG region was attached to hippocampal sections and separated by annotation for analysis. (C) The MFG
and STG regions were separated by annotation into cortex and white matter for glial profiling. (D) All cohort patients selected (N = 35) possessed high
ADNC at autopsy as defined in the methods. Patients were then separated into groups based on if they suffered from and survived a COVID-19 infection(COVID, N = 11), did not experience a COVID-19 infection and died during the same time period as COVID patients (Non-COVID, N = 12), or died prior to
SARS-CoV-2 arrival in the US on January 20, 2020 (Pre-2020, N = 12). Figures were created at BioRender.com.

onto the column) into RNAse-free tubes and stored at-20 °C for short-term use and − 80 °C for archival storage. Quality analysis was performed on the Bioanalyzer
2100 (Agilent, Denmark) platform, through which the
RNA integrity number (RIN) and DV200 were calculated.
Since we only planned to use the isolated RNA for highcycle count RT-qPCR detection of COVID transcripts
and not for other protocols requiring higher sensitivity(i.e. RNA-seq or accurate quantification of low-expressed
genes), we settled on a RIN cut-off of 3.0 for analyzed
cases. RNA concentration was determined using a Qubit
4 fluorometer (Invitrogen, USA), and stock solutions
were diluted to 100 ng/µL.
RT-qPCR detection of COVID-19 mRNA

RT-qPCR detection of COVID-19 transcripts was performed using the Promega GoTaq Probe 1-Step RT-qPCR
kit (Promega, USA) on a 384 well-plate. 5 µL reactions
were set up with 2.5 µL GoTaq probe master mix, 0.1 µL
GoScript RT mix, 0.2 each of premixed primer/probe forN1 and N2 (for viral detection reactions) or 0.2 each ofpremixed primer/probe for RNAseP and nuclease-freewater (for reactions to determine RNA template viability)(Integrated DNA Technologies, USA catalog #10006713),
and 2 µL of isolated RNA per well. Reactions were run in
duplicate with positive controls for SARS-CoV-2 N and
E RNA (Promega, USA catalog #AM2050) and negative
control wells with nuclease-free water in place of RNA

template. RT-qPCR was performed using the CFX384
Touch Real-Time PCR Detection System (BioRad, USA)with the cycling parameters included with the Promega
GoTaq Probe 1-Step RT-qPCR kit modified to run for
45 cycles of amplification. Detection of viral RNA wasdefined as if the fluorescence for the N1 + N2 reactions
for a case exceeded the threshold line and if the case generates a quantification cycle (Cq) value.
FFPE tissue DNA isolation and qPCR genotyping for APOE

To determine patients’ ApoE genotypes, gDNA wasextracted from a 10 μm scroll of FFPE temporal brain tissue using the QIAamp DNA FFPE Advanced Kit (Qiagen,Germany) following manufacturer-provided protocols.
35 µL of purified gDNA was eluted from the column,
concentration was determined using a Qubit 4 fluorometer (Invitrogen, USA), and the samples were diluted toa concentration of 5 ng/µL. Cases with stock concentrations of gDNA lower than 5 ng/µL could not be confidently genotyped and were excluded from analysis. qPCR
genotyping was performed in a 384-well plate using the
CFX384 Touch Real-Time PCR Detection System (BioRad, USA). 5 µL reaction wells were set up with 2.5 µL
Taqman Genotyping Master Mix (Fisher, USA catalog#4371355), 0.125 µL of Taqman SNP Genotyping Assayfor either rs7412 (Fisher, USA catalog #4351376) or
rs429358 (Fisher, USA catalog #4351376), 0.375 µL of
nuclease-free water, and 2 µL of 5 ng/µL gDNA. The plateVillareal et al. Journal of Neuroinflammation(2024) 21:202

Page 6 of 27

Table 3 IHC antibodies and pretreatments
Primary
Antibody

Manufacturer

Concerntration

SectionThickness

AntigenRetrieval
Solution
Tris and
0.05%
Tween
Tris and
0.05%
Tween
CitratePrimary
Incubation
PeriodOvernight@ 4°C

Secondary
Antibody

Secondary Incubation Period

Additional
notes

Ab5

In-house

1:50005 µm

Vector Laboratories ImmPRESS®

Overnight@ 4°C

Vector Laboratories ImmPRESS®

Described in
Levites et al.
2006 [49]Described
in Xia et al.,2020 [50]Overnight@ 4°C

Vector Laboratories ImmPRESS®

Tris and
0.05%
Tween
CitrateOvernight@ 4°C

Vector Laboratories ImmPRESS®

Overnight@ 4°C

Vector Laboratories ImmPRESS®

10 µm

Tris and
0.05%
Tween

Overnight@ 4°C

Vector Laboratories ImmPRESS®

30 minutes@ roomtemperature
30 minutes@ roomtemperature
30 minutes@ roomtemperature
30 minutes@ roomtemperature
30 minutes@ roomtemperature
30 minutes@ roomtemperature

7F2

In-house

1:5000

5 µm

Iba1

Abcam
- ab178847

1:8000

10 µm

P2RY12Millipore Sigma- HPA014518

1:1000

10 µm

CD68

Thermo Fisher- TA807199

1:500

10 µm

Ferritin(light andheavy
chains)GFAP

Millipore Sigma- F6136-1MG

1:1000

Encore- MCA-3E10

1:10005 µm

Tris and
0.05%
Tween

Overnight@ 4°C

Vector Laborato- 1 hour @ room
ries - BA-9200-1.5 temperature@ 1:1000

ALDH1L1

Thermo Fisher- UM500039

1:400

5 µm

Tris and
0.05%
Tween

Overnight@ 4°C

Vector Laborato- 1 hour @ room
ries - BA-9200-1.5 temperature@ 1:1000

Olig2

Abcam
- ab109186

1:100

5 µm

CitrateOvernight@ 4°C

Vector Laboratories ImmPRESS®

30 minutes@ roomtemperature

PLP1

MCAB396

1:10005 µm

CitrateOvernight@ 4°C

Vector Laboratories ImmPRESS®

CD3e

Invitrogen- MA5-14524

1:500

5 µm

CitrateOvernight@ 4°C

Vector Laboratories ImmPRESS®

30 minutes@ roomtemperature
30 minutes@ roomtemperature

GFAP (IF)Encore- MCA-3E10

1:10005 µm

CitrateOvernight@ 4°C

CD31 (IF)Proteintech− 11265-1-AP

1:500

5 µm

CitrateOvernight@ 4°C

Invitrogen Antimouse DyLight594 conjugated(35510)
Vector Laboratories ImmPRESS®

30 minutes@ roomtemperature

Add
ImmPRESS®
@ 1:1000
to secondary antibody
solution
Add
ImmPRESS®
@ 1:1000
to secondary antibody
solution
Agitate on
shaker during overnight
primary andsecondary
incubations

Develop with
Opal 520 @
1:1000 for 10
minutesLog2
foldchange-0.888
0.646

2.75
1.46

-0.717
-1.08

0.41

0.524
0.745

0.682

1.18

1.09

1.64
0.668
0.549
-0.708

1.2
0.357

1.15

-1.07
0.922

GAL3ST1-mRNA
TICAM1-mRNA

CHI3L1-mRNA
HTR5A-mRNA

PLXNB3-mRNA
MAG-mRNA

DMD-mRNA

POR-mRNACTSB-mRNABHLHE40-mRNA

VEGFA-mRNA

SPI1-mRNASERPINA3-mRNAAPLP2-mRNA
NPAS2-mRNA
ERMN-mRNAHMOX1-mRNA
TPI1-mRNA

DRD1-mRNA

TMEM88B-mRNA
STAMBPL1-mRNA

-1.03
-1.57

1.65
0.83

Lower
confidence
limit (log2)
-1.2
0.41

0.826
0.33
0.271
-1.07

0.55

0.599

0.348

0.274
0.384

-1.64
0.424

0.549

-0.492
1.42

1.75

1.8
0.542

2.46
1.01
0.828
-0.348

1.63

1.75

1.02

0.774
1.11

0.604

-0.409-0.591

3.85
2.08

Upper
confidence
limit (log2)-0.576
0.883

0.477
1.89

2.22

2.29
1.28

3.12
1.59
1.46
0.612

2.13

2.26

1.6

1.44
1.68

1.33

0.608
0.473

6.72
2.74

Linearfoldchange
0.54
1.57

0.32
1.34

1.46

1.5
1.13

1.77
1.26
1.21
0.477

1.46

1.52

1.27

1.21
1.31

1.16

0.491
0.337

3.13
1.78

Lower
confidence
limit (linear)
0.435
1.33

0.711
2.68

3.37

3.49
1.46

5.5
2.01
1.78
0.785

3.09

3.37

2.02

1.71
2.15

1.52

0.753
0.664

14.5
4.23

0.119
0.155

0.05790.0579

0.245
0.245
0.245
0.245

0.001560.00158

0.00118

0.341
0.341

0.281

0.000921 0.245
0.00109 0.274

0.000750.000869
0.000895
0.000908

0.000711 0.245

0.000653 0.245

0.000647 0.245

0.000501 0.245
0.00058 0.245

0.000454 0.245

lm.nb
lm.nb

lm.nb

lm.nb
lm.nb

lm.nb
lm.nb
lm.nb
lm.nb

lm.nb

lm.nb

lm.nb

lm.nb
lm.nb

lm.nb

lm.nb
lm.nb

lm.nb
lm.nb

lm.nb
lm.nb

BY.p.value Method

0.000171 0.155
0.000301 0.228

7.91E-05
0.00017Upper
P-valueconfidence
limit (linear)
0.671
1.58E-05
1.84
2.56E-05

Primed Microglia

Myelogenesis
Apoptosis, NF-kappaB Signaling, NO Metabolism andSignaling
Astrocyte Markers
Calcium Signaling, Neuroactive Ligands and Receptors,
Serotonergic Synapse
Neurogenesis
Myelogenesis, Oligodendrocyte
DifferentiationMaturation
Blood Brain Barrier, Cytoskeletal
Dynamics, Neurogenesis
NO Metabolism and Signaling
Apoptosis, Autophagy,
Microglia Neurodegenerative
Phenotype (MGnD), Primed
Microglia, Stage 1 DAM
Circadian Signaling, Microglia
Neurodegenerative Phenotype(MGnD)
Angiogenesis, Hypoxia, Microglia Neurodegenerative Phenotype (MGnD), Neurogenesis
Microglia Neurodegenerative
Phenotype (MGnD)
Astrocyte Markers
Primed Microglia
Circadian Signaling
Cytoskeletal Dynamics, Oligodendrocyte Markers
Hypoxia
Glucose Metabolism, Primed
Microglia
Astrocyte DifferentiationFunction, Calcium Signaling, Dopaminergic Synapse, Neuroactive
Ligands and Receptors

Gene.sets

NM_001146685.1:222
NM_020799.2:1600

NM_000794.3:1970

NM_002133.2:781
NM_000365.5:153

NM_001085.4:1220
NM_001642.2:1380
NM_002518.3:1458
NM_001304344.1:440

NM_003120.1:730

NM_001025366.1:1325

NM_003670.2:716

NM_000941.2:114
NM_147780.2:1054

NM_004014.2:1334

NM_001163257.1:3663
NM_001199216.1:1780

NM_001276.2:475
NM_024012.2:776

NM_004861.1:1378
NM_014261.1:518

probe.ID(2024) 21:202

0.294
0.254

0.307

0.31
0.588
0.0944 0.172

0.417
0.172
0.142
0.183

0.275

0.294

0.171

0.128
0.184

0.0988 0.216

0.157
0.25

0.563
0.319

std
error
(log2)
0.159
0.121

Table 4 Top 25 differentially expressed genes in COVID vs. Non-COVID in Hippocampus

Villareal et al. Journal of Neuroinflammation
Page 7 of 27

lm.nb
lm.nb
0.378
0.379

NM_000291.2:1030
NM_007355.3:1530

NM_000533.3:758

Astrocyte DifferentiationFunction, Myelogenesis,
Oligodendrocyte DifferentiationMaturation, Oligodendrocyte Markers
Glucose Metabolism
Cytoskeletal Dynamics, Inflammasome, Proteotoxic Stress
lm.nb
0.378

NanoString nCounter® gene expression analysis

Results
To identify the effect of COVID-19 infection on regions
affected by AD pathology at varying points of diseaseprogression, four regions, including the medial frontal
gyrus (MFG), superior temporal gyrus (STG), hippocampus, and combined fusiform and inferior temporal gyri
(FG + ITG), were sampled for immunohistochemical analysis (Fig. 1A). As previous literature has heavily implicated the choroid plexus and blood-CSF barrier in thetransmission of peripheral COVID-19-associated inflammatory signals to the brain [14], the selected regions were

0.746
0.697

PLP1-mRNA

PGK1-mRNA
HSP90AB1-mRNA

std
error
(log2)
0.22

1.16
1.09

Stock FFPE RNA was diluted with non-DEPC-treatednuclease-free water (Invitrogen, USA) using the previously collected Qubit and DV200 values to 20 ng/mL
of mRNA with length ≥ 200 nucleotides as describedby NanoString [44]. 5 µL of each diluted sample was
hybridized with the NanoString nCounter® Glial Profiling Panel (XT HS Glial Profiling #115000429) in a thermocycler for 18 h at 65 °C (heated lid at 72 °C) following
NanoString’s provided protocols [31, 45]. Detection using
the NanoString nCounter® Profiler and data analysis andquality assessment via nSolver® was performed exactly as
previously described [31].

0.334
0.307

1.26
1.24

RNA was isolated from all COVID and Non-COVID
patients within the cohort (N = 23) using the High Pure
FFPE RNA Isolation Kit (Roche Life Science, Germany).
Two 20 μm thick scrolls of FFPE tissue were collectedfrom the hippocampus and MFG of each patient andplaced into cassettes. RNA was extracted from the scrolls
following the manufacturer-provided protocols [43]. The
concentration of extracted RNA was quantified using the
Qubit 4 fluorometer (Invitrogen, USA), and RNA quality (DV200) was assessed via Bioanalyzer 2100 (Agilent,
Denmark). Samples were stored in RNAse-free tubes at-20 °C overnight prior to running the nCounter® assays,
and were stored at -80 °C for long-term archival storage.

0.211
0.199

1.68
1.62

2.23
2.13

0.00192
0.00211

probe.ID
Gene.sets

Upper
P-valueconfidence
limit (linear)
0.785
0.0019FFPE tissue RNA isolation and quality analysis

Upperconfidence
limit (log2)
-0.35

Lower
confidence
limit (linear)
0.432

was sealed using a microseal ‘B’ seal (BioRad, USA catalog #MSB1001) and cycled 40 times with the following
parameters: 10 min AmpliTaq Gold, UP Enzyme Activation @ 95 °C (1x); 15 s of denaturing @ 95 °C and 1 min of
annealing/extension @ 60 °C (40x). Nucleotide identity ateach SNP and genotype was determined using previously
defined ratios between VIC: FAM fluorescence.

Lower
confidence
limit (log2)-1.21

Linearfoldchange0.582

Page 8 of 27

Statistics

Log2
fold
change-0.78

Table 4 (continued)(2024) 21:202

BY.p.value Method

Villareal et al. Journal of Neuroinflammation

Statistical analysis was performed on GraphPad Prism
9 and individual parameters are reported in the figure
captions.
Cohort overview and regional differences in COVID-AD

Villareal et al. Journal of Neuroinflammation(2024) 21:202

also arranged in terms of anatomical proximity to thelateral ventricles [46–48] (Fig. 1A), with the hippocampus being the closest and the MFG the furthest. The grey
matter (GM) of the hippocampus was further classified
into the four cornu ammonis (CA) subfields (CA1-CA4),
subiculum (SUB), and entorhinal cortex (EC) (Fig. 1B)
and the STG and MFG into cortex (grey natter) andwhite matter (WM) regions (Fig. 1C). FG + ITG regions
were attached to hippocampal sections when collected
at autopsy but were separated during QuPath annotation and considered as their own independent regions
during quantification (Fig. 1B). These regions wereassessed in patients with high ADNC who were separated into groups based on if they suffered and recoveredfrom at least one COVID-19 infection (COVID), did notreport a COVID-19 infection and died during the sametime period as COVID patients (Non-COVID), and AD
patients who died prior to SARS-CoV-2 arrival in the US(Pre-2020) (see methods for additional selection criteria)(Fig. 1D). Additionally, patients in all three cohorts were
negative for SARS-CoV-2 mRNA via RT-qPCR detection,
indicating that the observed changes below persist in theparenchyma following the resolution of active infection.
COVID-AD survivors show elevated Aβ plaque burden in
select brain regions

To test if COVID-19 infection impacted AD-related protein pathologies, we quantified the burden of Aβ plaques(anti-Ab5 [49]) (Fig. 2A) and tau/NFT (anti-7F2 [50])(Fig. 2F) in COVID, Non-COVID and Pre-2020 patients.
Intriguingly, COVID patients showed higher Aβ coverage in the FG + ITG cortices (Fig. 2C) and EC (Fig. 3C)
compared to Non-COVID and Pre-2020 patients. A similar trend toward elevated Aβ burden was also observedin the other brain regions evaluated (Fig. 2B, D-E). NFT
coverage was largely unchanged between patient groups
across all regions and hippocampal subfields (Figs. 2G-J
and 3F).
COVID-AD is characterized by elevated numbers ofhippocampal microglia

To get an initial picture of microglia in COVID patients,
we performed IHC analysis of ionized calcium binding adaptor molecule 1 (Iba1) (Fig. 4A) which is a cytoplasmic protein that acts as a pan-microglial marker
in humans [51]. Using automated cell counting via the
QuPath software [31] (see methods section), an increase
in Iba1-positive microglia was observed in the hippocampi of COVID compared to Non-COVID (Fig. 4B),
with the latter not differing significantly from Pre-2020
patients, validating the Non-COVID group as being representative of a non-infected population with respect tomicroglia numbers.

Page 9 of 27

These changes were most apparent in the CA4,
CA2, CA1 subfields and the subiculum of COVID AD(Fig. 5C). While other regions further away from the CP
exhibit similar trends towards higher microglia numbers in COVID (Fig. 4C-E), these only reached statistical significance in the MFG WM (Fig. 5B), suggestingthat anatomically “deeper” regions closer to the choroid
plexus could be affected more acutely by a systemic infectious stimulus. Additionally, the number of Iba1-positive
microglia was not significantly associated with Aβ plaqueburden (Supp. Figure 1 A), confirming that our findings
were not primarily driven by patient-to-patient variancesin pathology.
Compounding loss of microglial homeostasis in COVID-AD
patients

To further characterize the activation state of microglial
populations in the COVID group, we evaluated purinergic receptor P2Y12 (P2RY12) (Fig. 4F), a transmembraneprotein expressed by highly ramified microglia that has
previously been shown to decrease in expression withincreasing microglial activation [52, 53]. Individuals suffering from AD have been shown to have reduced numbers of homeostatic, P2RY12-positive microglia [18, 20]
as a response to disease -specific protein pathology. Inline with our Iba1-data, COVID patients showed reduced
numbers of P2RY12-positive microglia in the hippocampus (Fig. 4G) and the FG/ITG cortex (Fig. 4H) as compared to the Non-COVID and Pre-2020 patients. This
loss of microglial homeostasis was mainly detected in the
CP-adjacent CA4-CA2 regions but could also be detectedin the EC (Fig. 5F). STG and MFG cortices did not significantly differ between groups in terms of P2RY12-positive
microglia (Fig. 4I-J).
To study additional markers of microglial activation,
we next analyzed cluster of differentiation 68 (CD68) –a lysosomal transmembrane protein and a marker forphagocytic activity in microglia [54, 55] (Fig. 6A). This
analysis did not exhibit significant differences in numbers of positive cells between COVID and Non-COVID
groups irrespective of brain region (Fig. 6B-E), suggestingthat there is no major association between COVID infection and phagocytic activity of microglia in AD patients.
Next, we studied ferritin, a primarily cytosolic protein
complex composed of light and heavy (FTH1) chainsenabling iron uptake and storage [56] (Fig. 6F). Previous studies have demonstrated an association between
microglial ferritin expression and microglial dystrophy inpatients with AD [57–59].
Analyzing ferritin-positive microglia, we noticed atrend towards increased microglia dystrophy in the hippocampus, FG + ITG cortices, and MFG cortex of COVID
compared to the Non-COVID and Pre-2020 groups(Fig. 6G-J), with significantly higher numbers of Ferritin

-0.2291.5-0.341-0.396-0.315-0.416

0.979

0.777

-0.252-0.442-0.628-0.408-0.527-0.571-0.32-0.311

ROCK2-mRNA
HSPA1A-mRNA

TBK1-mRNA

NLK-mRNATIAM1-mRNA

TRIM37-mRNA

CD44-mRNA

VEGFA-mRNA

ACADM-mRNA
GSN-mRNAP2RY11-mRNA

Figure 4-mRNA
KCTD12-mRNA
KCND1-mRNA
USP2-mRNA
BECN1-mRNA

0.182

0.236

-0.732-0.69-0.554

-0.59

-0.74
-0.973-1.06-0.592-0.576-1.13-0.0772-0.0803-0.0861-0.0479-0.0464-0.124-0.0569-0.0932

1.37

1.72

-0.101-0.101-0.0767-0.0915-0.0652
2.58-0.167-0.0967-0.142Upper
confidence
limit (log2)-0.135
3.5
3.27
2.44

0.753
0.694
0.673
0.801
0.806

0.647

0.84
0.736

1.71

1.97

0.749

0.76
0.804

0.79

0.853
2.82

0.735
0.836
0.752

Linearfoldchange
0.821
4.67
4.17
2.86

0.599
0.509
0.481
0.664
0.671

0.456

0.734
0.578

1.13

1.18

0.602

0.62
0.681

0.664

0.762
1.32

0.607
0.747
0.623

Lower
confidence
limit (linear)
0.74
1.94
1.8
1.5

0.948
0.946
0.942
0.967
0.968

0.918

0.961
0.937

2.59

3.3

0.932

0.932
0.948

0.939

0.956
6

0.891
0.935
0.906

Upper
confidence
limit (linear)
0.91
11.3
9.68
5.43
Cannabinoid Signaling
Astrocyte Markers
Astrocyte Markers
Cytokines, Interferon Signaling,
Microglia Neurodegenerative Phenotype (MGnD), Microglial Markers
Proteotoxic Stress
Apoptosis, Microglial Markers
Calcium Signaling, Dopaminergic
Synapse, Glutamatergic Synapse,
Microglia Neurodegenerative
Phenotype (MGnD), Serotonergic
SynapseCell Migration, Wnt Signaling
Microglial Markers, Proteotoxic
StressAutophagy, Cytokines, NF-kappaB
Signaling
Wnt Signaling
Cytoskeletal Dynamics, Neurogenesis, Neurotrophin Signaling
Antigen Processing and
Presentation
Astrocyte Markers, Interferon Signaling, Primed Microglia
Angiogenesis, Hypoxia, Microglia
Neurodegenerative Phenotype(MGnD), Neurogenesis
Lipid Metabolism
Cytoskeletal Dynamics, Oligodendrocyte DifferentiationMaturation,
Phagocytosis
Neuroactive Ligands and Receptors,
Purinergic Signaling
Myelogenesis, Neurogenesis
GABAergic Synapse
Ion Transport
Microglial Markers, Primed Microglia
Autophagy
0.02490.031
0.03130.03150.03150.02360.01940.02150.01830.01740.01710.01560.01710.01410.01220.0137

1
1
1
1
1

1

1
1

1

1

1

1
1

1

1
1

0.00495 1
0.00507 1
0.00697 1

lm.nb
lm.nb
lm.nb
lm.nb
lm.nb

lm.nb

lm.nb
lm.nb

lm.nb

lm.nb

lm.nb

lm.nb
lm.nb

lm.nb

lm.nb
lm.nb

lm.nb
lm.nb
lm.nb

lm.nb
lm.nb
lm.nb
lm.nb

0.00123
0.00252
0.003180.0042611
1
1Gene.sets

P-value BY.p.value method

NM_014845.5:1060
NM_138444.2:450
NM_004979.4:2400
NM_001243759.1:676
NM_003766.2:810

NM_002566.4:729

NM_000016.5:1289
NM_000177.4:1075

NM_001025366.1:1325

NM_001001392.1:429

NM_015294.4:1910

NM_016231.4:618
NM_001353691.1:4520

NM_013254.2:1610

NM_004850.3:3140
NM_005345.5:1875

NM_014413.2:1685
NM_003793.3:655
NM_080425.1:1910

NM_004544.3:876
NM_001085.4:1220
NM_001276.2:475
NM_001964.2:838

probe.ID(2024) 21:202

0.169
0.228
0.247
0.139
0.135

0.257

0.0995 -0.447
0.178 -0.791

0.304

0.379

0.161

0.15
0.122

0.127

0.0835 -0.393
0.556 0.406

0.141 -0.721
0.0827 -0.421
0.138 -0.682-0.444-0.259-0.412

EIF2AK1-mRNA
CTSF-mRNA
GNAS-mRNA

Lower
confidence
limit (log2)-0.435
0.953
0.848
0.588

std
error
(log2)
0.0764
0.649
0.619
0.473

Log2
fold
changeNDUFA10-mRNA -0.285
SERPINA3-mRNA 2.22
CHI3L1-mRNA
2.06
EGR1-mRNA
1.52

Table 5 Top 25 differentially expressed genes in COVID vs. Non-COVID in MFG

Villareal et al. Journal of Neuroinflammation
Page 10 of 27

probe.ID

NM_000237.2:736
Microglia Neurodegenerative Phenotype (MGnD), Stage 2 DAM
LPL-mRNALog2
foldchange-0.499

Table 5 (continued)

std
error
(log2)
0.218

Lower
confidence
limit (log2)-0.927Upper
confidence
limit (log2)-0.0712

Linearfoldchange0.708

Lower
confidence
limit (linear)
0.526

Upper
P-value BY.p.value method
confidence
limit (linear)
0.952
0.0327 1
lm.nb

Gene.sets

Villareal et al. Journal of Neuroinflammation(2024) 21:202

Page 11 of 27

positive cells observed in the CA2 subfield (Fig. 7F). The
number of positive cells for each of these three markers was again compared against Aβ burden (Supp. Figure 1E-P), revealing no significant associations between
either marker and pathological burden.
Astrocyte populations are universally diminished in
COVID-ADInterested in revealing a more comprehensive image ofthe neuroimmune status possessed by COVID patients,
we performed IHC analysis of various astrocyte-lineage
markers in the hippocampus and MFG. By analyzing
astrocytes from two regions at opposite distances from thelateral ventricles, we sought to determine whether proximity to the CP was similarly associated with more acute differences between groups as observed within the microglial
phenotypes. We first compared glial fibrillary acidic protein (GFAP) between groups (Fig. 8A), a marker for a subpopulation of reactive astrocytes that has been observed
to proliferate in relation to Aβ pathological burden [26,60–62]. Interestingly, COVID patients had significantly
fewer numbers of hippocampal GFAP-positive astrocytes
than both Non-COVID and Pre-2020 patients (Fig. 8B),
with the most acute differences occurring in the CA4 andCA1 hippocampal subfields (Fig. 8G). While no difference
in GFAP-positive astrocyte quantity was observed in the
MFG cortex (Fig. 8C), COVID patients possessed significantly lower numbers of GFAP-positive astrocytes in the
more CP-proximal MFG white matter (Fig. 8H).
In order to determine whether the reduction in astrocyte number was specific to the reactive sub-population
or if astrocytes were universally affected in COVID
patients, we decided to evaluate aldehyde dehydrogenase
1 family member L1 (ALDH1L1), a pan-astrocytic marker[63–67] (Fig. 8D). Concordant with our GFAP data,
COVID patients had the least number of hippocampal
ALDH1L1-positive cells out of the three groups (Fig. 8E),
with significantly lower numbers as compared to the Pre2020 cohort at the whole-region level. This astrocytic
loss was most evident in the CA3 and EC subfields, anda difference between Pre-2020 and Non-COVID was
observed in CA2 (Fig. 8I). No difference in MFG cortex(Fig. 8F) and white matter (Fig. 8J) ALDH1L1-positive
astrocytes was observed. GFAP and ALDH1L1 were notsignificantly associated with plaque burden (Supp. Figure 1Q-R), suggestive of it not being a primary driving
force for the observed astrocytic changes.
Interested in further defining whether the observed
astrocytic loss in COVID patients was the consequenceof whole-region vulnerability or more localized changes
at the BBB and parenchymal microvasculature, we performed immunofluorescent proximity analysis of platelet
endothelial cell adhesion molecule (CD31 or PECAM1)-positive endothelial cells and astrocytes (Fig. 9A-B).

Villareal et al. Journal of Neuroinflammation(2024) 21:202

Page 12 of 27

Fig. 2 COVID-AD survivors show elevated Aβ plaque burden in select brain regions. (A) Representative images of Ab5 (Aβ plaques) immunopositivity inthe hippocampus grey matter, FG + ITG cortex, STG cortex, and MFG cortex of cohort patients. Inserts depict morphology of pathological protein deposits
in each patient cohort. Tissue coverage was quantified in 5 μm-thick FFPE sections as described in the methods. Scale bar = 300 μm. Percent coverageof cortex (grey matter in the case of hippocampus) by Ab5 in the (B) hippocampus (Pre-2020 N = 12, Non-COVID N = 12, and COVID N = 11), (C) FG + ITG(Pre-2020 N = 9, Non-COVID N = 11, and COVID N = 10), (D) STG (Pre-2020 N = 12, Non-COVID N = 12, and COVID N = 11), and (E) MFG (Pre-2020 N = 12,
Non-COVID N = 12, and COVID N = 11) of the cohort patients. (F) Representative images of 7F2 (NFT) immunopositivity in the same regions and patients.
Scale bar = 100 μm. (G-J) Percent coverage of the cortex in cohort patients by 7F2 in the (G) hippocampus (Pre-2020 N = 12, Non-COVID N = 12, and
COVID N = 11), (H) FG + ITG (Pre-2020 N = 10, Non-COVID N = 11, and COVID N = 9), (I) STG (Pre-2020 N = 12, Non-COVID N = 12, and COVID N = 11), and(J) MFG (Pre-2020 N = 12, Non-COVID N = 12, and COVID N = 11). Comparisons were performed using an ordinary one-way ANOVA with Tukey’s multiple
comparisons test, with a single pooled variance. Each group’s standard deviation is represented by error bars. “ns” = no significance, *p < 0.05, **p < 0.01,***p < 0.001. Any comparisons lacking asterisks have no significance. The hippocampus regions contains the average of each patients’ subfield quantifications (Figs. 3C and F)

Pre-2020 patients were excluded from this analysis due tothe presence of auto fluorescent artifacts in multiple cases
within the cohort. CD31 is a transmembrane protein thatacts as an endothelial cell marker throughout the body,labeling blood vessels within the brain parenchyma [68].
We decided to use GFAP as our marker for astrocytes due
to its better morphological labeling of astrocyte processes
and endfeet in combination with its more acute changes
in the COVID cohort. Our analysis revealed no significant
difference in hippocampal CD31-positive vessel coverage (Fig. 9C) and colocalization of CD31 and GFAP (i.e.
astrocyte endfoot coverage) (Fig. 9E) between COVID

and Non-COVID patients. Additionally, there was noobserved loss of GFAP-positive astrocytes in proximity
to cortical vasculature (Fig. 9F), suggestive of an alternate,hippocampus-specific factor not primarily associated with
the BBB resulting in the loss of astrocytes observed in
COVID patients.
COVID-19 infection is associated with region-specificdysregulation of oligodendrocyte and myelin-associatedpathways in AD

Seeking to evaluate potential alterations in the transcriptome of COVID patients, we performed bulk mRNA

Villareal et al. Journal of Neuroinflammation(2024) 21:202

Page 13 of 27

Fig. 3 Quantification of white matter and hippocampal subfield AD pathology burden. QuPath quantification of plaque burden (Ab5) (A-B) and pTauburden (7F2) (D-E) in the STG and MFG white matter of cohort patients (Pre-2020 N = 12, Non-COVID N = 12, and COVID N = 11). Comparisons were performed using an ordinary one-way ANOVA with Tukey’s multiple comparisons test, with a single pooled variance. Hippocampal subfields were separated
by annotation and analyzed in a similar manner for Ab5 coverage (C) and 7F2 coverage (F). Subfield comparisons were made using an ordinary two-way
ANOVA with Tukey’s multiple comparisons test, with a single pooled variance. “ns” = no significance, *p < 0.05, **p < 0.01, ***p < 0.001. Any comparisons
lacking asterisks are non-significant

analysis using the Human Glial Profiling panel on the
NanoString nCounter® platform. Consecutive FFPE sections from Hippocampus/FG + ITG and MFG from thesame patients in the COVID (N = 11, per region) andNon-COVID (N = 12, per region) groups were selected
for analysis to examine regional transcriptomic differences at the extremes of the CP-proximity and AD axes.
While disease-associated microglial and astrocytic pathways (“Stage 1 DAM”, “Stage 2 DAM”, “Microglia Neurodegenerative Phenotype (MGnD)”, “A1 Astrocyte”)(Fig. 10C) were only mildly upregulated in COVID (alsosee Table 4), we noticed a dysregulation in oligodendrocyte and myelogenesis systems (“Oligodendrocyte
Markers”, “Oligodendrocyte DifferentiationMaturation”,
and “Myelogenesis”) (Fig. 10C-E). Differently expressed

genes (DEGs) within these pathways were downregulated in association with COVID-19 infection (Fig. 12CE), suggestive of oligodendrocyte lineage dysfunction
as a defining characteristic of the COVID tissue microenvironment. To evaluate these findings at the protein
level, we performed IHC analysis of oligodendrocytetranscription factor 2 (Olig2), a protein marker for oligodendrocyte lineage cell types (Fig. 10F) [69–72]. Loss of
Olig2-positive staining in COVID patients was observed
within hippocampal subfields (Fig. 12L) but not at thewhole-tissue level (Fig. 10G) and Olig2 positivity did
not significantly correlate with plaque coverage (Supp.
Figure 1S).
Previous literature has also identified impaired myelination as a potential upstream event and risk factor

Villareal et al. Journal of Neuroinflammation(2024) 21:202

Page 14 of 27

Fig. 4 COVID-AD is characterized by elevated numbers of hippocampal microglia and loss of homeostatic microglia. (A) Images of Iba1-positive microglia in the hippocampus, FG + ITG, STG, and MFG of Pre-2020, Non-COVID, and COVID patients. Inserts depict cellular morphology in each patient cohort.
Iba1-positive microglia appeared morphologically diverse, ranging from moderately ramified to amoeboid in terms of process extension. Quantificationof Iba1-positive microglia per mm2 in the cortex/grey matter of (B) hippocampus (Pre-2020 N = 12, Non-COVID N = 12, and COVID N = 11), (C) FG + ITG(Pre-2020 N = 9, Non-COVID N = 11, and COVID N = 10), (D) STG (Pre-2020 N = 12, Non-COVID N = 12, and COVID N = 11), and (E) MFG (Pre-2020 N = 12,
Non-COVID N = 12, and COVID N = 11). Hippocampal values were obtained by averaging the Iba1 quantifications in the subfields of each patient as reported below (Fig. 5C). (F) Panel depicting P2RY12-positive microglia distribution in the regions and patients mentioned above. Morphologically, P2RY12positive microglia were highly ramified and possessed long-reaching processes. Quantification of P2RY12-positive microglia per mm2 in the cortex/grey
matter of (G) hippocampus (Pre-2020 N = 12, Non-COVID N = 12, and COVID N = 11), (H) FG + ITG (Pre-2020 N = 9, Non-COVID N = 11, and COVID N = 10),(I) STG (Pre-2020 N = 12, Non-COVID N = 12, and COVID N = 11), and (J) MFG (Pre-2020 N = 12, Non-COVID N = 12, and COVID N = 11) in each experimentalgroup. Subfield quantification data for the hippocampus region is provided below (Fig. 5F). IHC was performed in 10 μm-thick FFPE sections for both Iba1
and P2RY12. Comparisons were performed using an ordinary one-way ANOVA with Tukey’s multiple comparisons test, with a single pooled variance. Eachgroup’s standard deviation is represented by error bars. “ns” = no significance, *p < 0.05, **p < 0.01, ***p < 0.001. Any comparisons lacking asterisks have nosignificance. Scale bars = 100 μm

associated with AD pathogenesis [73]. Galactose-3-Osulfotransferase 1 (GAL3ST1), also known as cerebroside sulfotransferase, is a significant enzyme involved inmyelogenesis and is the final step in producing sulfatide,a glycosphingolipid representing around 30% of myelin
lipids in combination with galactocerebroside [74, 75].
GAL3ST1 is the most significantly downregulated genein COVID patients (Figs. 10A and 12G) and is of particular importance, with reduced CNS sulfatide levels potentially acting as a prodromal indicator of AD [75–77].

Seeking a general picture of myelin health in COVID
patients, we performed IHC analysis of the percent
area covered by proteolipid protein 1 (PLP1) (Fig. 10F)
which, along with myelin basic protein, makes up 68%
of myelin proteins [78]. No difference in PLP1 coverage
was observed between groups in whole hippocampus(Fig. 10H) or subfields (Fig. 12M) and coverage did notcorrelate with plaque burden (Supp. Figure 1T), indicative of a more conserved myelin response to infectionthat was not primarily driven by Aβ pathology.

Villareal et al. Journal of Neuroinflammation(2024) 21:202

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Fig. 5 White matter and hippocampal subfield analysis of total and homeostatic microglia in COVID-AD. Iba1-positive (A-B) and homeostatic P2RY12positive (D-E) microglia per mm2 in the STG and MFG white matter of Pre-2020 (N = 12), Non-COVID (N = 12), and COVID (N = 11) patients. Comparisonswere made using an ordinary one-way ANOVA with Tukey’s multiple comparisons test, with a single pooled variance. Hippocampal subfield quantification of Iba1-positive (C) and P2RY12-positive (F) microglia per mm2 was also performed to determine spatial location of profile changes in the hippocampus. Subfield comparisons were made using an ordinary two-way ANOVA with Tukey’s multiple comparisons test, with a single pooled variance.
Each group’s standard deviation is represented by error bars. “ns” = no significance, *p < 0.05, **p < 0.01, ***p < 0.001. Any comparisons lacking asterisks
are non-significant

TIR domain-containing adaptor molecule 1 (TICAM1, also known as TRIF) is another DEG of interest,
encoding an adaptor molecule to most toll-like receptors (TLR), some of which possess the capability to detect
single-strand RNA viruses [79–81]. We demonstrate that
COVID-19 patients possess subtly elevated TICAM-1
expression (Figs. 10A and 12H) and NF-kappaB signaling in the hippocampus (Fig. 10C), the latter of which isthe direct effect of the TLR-TICAM-1 signaling cascade.
Interested in determining whether the upregulation ofthese immune signaling pathways corresponded withincreased T-cell surveillance and invasion of the hippocampal parenchyma in COVID patients, we performed
IHC quantification of CD3e-positive cells to cover both

CD8 + and CD4 + lineages (Fig. 11A). We did not observea difference in the quantity of hippocampal T-cells acrosscohort groups (Fig. 11B-C), suggesting that the previously described neuroimmune changes are largely organized by innate, rather than adaptive, signaling cascades.
Compared to the hippocampus, the gene expression
changes observed in the more CP-distant MFG were
muted and less conserved between patients (Figs. 10A-B
and 12A-B). Pathway differences in the MFG of COVID
and Non-COVID patients were much less pronounced(Fig. 10C), and the oligodendrocyte lineage dysregulation observed in the hippocampus was absent (Fig. 10C).
DEGs within the MFG were also more attenuated, with
only a few astrocyte-associated genes (SERPINA3 andVillareal et al. Journal of Neuroinflammation(2024) 21:202

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Fig. 6 Assessment of microglial activation profiles in COVID-AD patients. (A) Representative images of CD68-positive microglia in hippocampus grey
matter, FG + ITG cortex, STG cortex, and MFG cortex in our three patient groups. Inserts depict cellular morphology for each marker. CD68-positive microglia morphology was largely spherical and ameboid, with a distinct loss of process length/extension. QuPath quantification of CD68-positive microglia per
mm2 in (B) hippocampus grey matter (Pre-2020 N = 12, Non-COVID N = 12, and COVID N = 11), (C) FG + ITG cortex (Pre-2020 N = 8, Non-COVID N = 12, and
COVID N = 10), (D) STG cortex (Pre-2020 N = 12, Non-COVID N = 12, and COVID N = 11), and (E) MFG cortex (Pre-2020 N = 12, Non-COVID N = 12, and COVID
N = 11) in cohort patients. Measurements for the hippocampus come from the average of subfields CA4-EC (Fig. 7C). (F) Panel depicting ferritin-positive
microglia in the hippocampal grey matter, FG + ITG cortex, STG cortex, and MFG cortex of our three patient groups. Ferritin-positive microglia appearedmorphologically dystrophic, with retracted and “shriveled” processes. QuPath quantification of ferritin-positive microglia per mm2 in (G) hippocampus
grey matter (Pre-2020 N = 12, Non-COVID N = 12, and COVID N = 11), (H) FG + ITG cortex (Pre-2020 N = 8, Non-COVID N = 12, and COVID N = 10), (I) STG
cortex (Pre-2020 N = 12, Non-COVID N = 12, and COVID N = 11), and (J) MFG cortex (Pre-2020 N = 12, Non-COVID N = 12, and COVID N = 11). Hippocampus
means were derived from subfield averages (Fig. 7F). IHC was performed was performed in 10 μm-thick FFPE sections for both markers. Comparisonswere performed using an ordinary one-way ANOVA with Tukey’s multiple comparisons test, with a single pooled variance. Each group’s standard deviation is represented by error bars. “ns” = no significance, *p < 0.05, **p < 0.01, ***p < 0.001. Any comparisons lacking asterisks have no significance. Scalebars = 100 μm

CHI3L1) overlapping with the profile observed in the
hippocampus (Fig. 12B, J-K). Interestingly, astrocyte andpro/anti-inflammatory pathways (“A2 Astrocyte”, “Astrocyte Markers”, “Chemokines”, “Complement System”)
were some of the upregulated systems amongst mostly
neutral/downregulated pathways in the COVID MFG(Fig. 10C). Relative comparisons between cytokine transcripts included in the nCounter panel were analyzed,
with trends towards increased CCL2 and IFNAR1 being
observed in COVID patients (Fig. 12N).

Conclusions
Sharing a strikingly similar at-risk population, COVID19, AD, and their intersection represent a rapidly growing challenge for the world’s aging population. In thisstudy, we conducted extensive immune and glial profilingof severe AD patients who survived systemic infectionin the form of COVID-19 across multiple disease-relevant brain regions. While related, informative research
has previously been performed on this topic [82], ourdata specifically focuses on the long-term effects ofVillareal et al. Journal of Neuroinflammation(2024) 21:202

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Fig. 7 White matter and hippocampal subfield microglial activation profiles. Measurements for CD68-positive (A-B) and Ferritin-positive (D-E) microglia
per mm2 in the STG and MFG white matter of Pre-2020 (N = 12), Non-COVID (N = 12), and COVID (N = 11) patients. Comparisons were made using an ordinary one-way ANOVA with Tukey’s multiple comparisons test, with a single pooled variance. We also performed hippocampal subfield quantification of
CD68-positive (C) and Ferritin-positive (F) microglia per mm2. Subfield comparisons were made using an ordinary two-way ANOVA with Tukey’s multiple
comparisons test, with a single pooled variance. Each group’s standard deviation is represented by error bars. “ns” = no significance, *p < 0.05, **p < 0.01,***p < 0.001. Any comparisons lacking asterisks are non-significant

COVID-19 infection and recovery on neuroimmune
states and potential progression of AD pathologies. In
particular, we focused only on patients who did not die
while experiencing a COVID-19 infection and who were
COVID-negative at time of autopsy. Additionally, wewere interested in the chronic neuroimmune alterations
experienced by COVID patients and focussed on specificimmunophenotypic shifts across cell types in the context
of these two conditions.
Initial comparison of AD pathologies between Pre2020, Non-COVID, and COVID patients revealed
significant FG + ITG and EC elevation of Aβ pathological burden associated with COVID-19 at an averageof 4.55 months post-infection. As one of the earliest
brain structures affected by AD pathophysiology [83],EC parenchyma is already severely vulnerable to additional changes in the neural environment, potentiallybeing ‘pushed over the edge’ into a more advanced disease state before other regions by elevated, peripheryoriginating inflammatory signals. Elevated burdens of
AD pathological hallmarks were also localized within
the grey matter layers of the hippocampus, a trend thatis also reflected throughout our glial analyses. With amajority of AD pathological changes occurring withinthese layers, the suggestion that COVID-associatedneurological insults are also localized here lends tothe idea of it being involved in disease progression.
Importantly, the regions experiencing the most significant increase in Aβ plaque coverage followed a pattern distinct from those of the glial population changes

Villareal et al. Journal of Neuroinflammation(2024) 21:202

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Fig. 8 Astrocyte populations are universally diminished in COVID-AD. (A) Appearance of GFAP-positive astrocytes in the hippocampus and MFG of
Pre-2020, Non-COVID, and COVID patients. GFAP-positive astrocytes displayed extended processes with filamentous patches in the vicinity of mostcells. (B) QuPath quantification of hippocampal GFAP-positive cells revealed a significant reduction of reactive astrocyte number in COVID patients (Pre2020 N = 12, Non-COVID N = 12, and COVID N = 11). Hippocampal measurements were calculated from an average of CA4-EC subfield measurements(Fig. 8G). (C) There was no observed difference in GFAP positivity across groups in the MFG cortex. (D) Quantification of ALDH1L1-positive astrocytes
was performed in the hippocampi and MFG of our cohort groups to assess the extent to which the pan-astrocyte lineage is affected in COVID patients.
ALDH1L1-positive astrocytes displayed similar morphology to their GFAP counterparts but lacked the previously observed filamentous patches. (E) QuPath quantification of ALDH1L1-positive astrocytes in the hippocampus revealed a significant reduction in number of the cell type in the COVID group(Pre-2020 N = 12, Non-COVID N = 12, and COVID N = 11). (F) Quantification of ALDH1L1-positive astrocytes in the MFG cortex revealed no differences
across groups. Group averages were derived from subfield measurements (Fig. 8I). IHC was performed for both markers in 5 μm-thick FFPE sections. (G-I)
Hippocampal subfield and (H-J) MFG white matter quantification of GFAP-positive and ALDH1L1-positive astrocytes. Comparisons were performed using
an ordinary one-way ANOVA with Tukey’s multiple comparisons test, with a single pooled variance or an unpaired student’s t-test. Each group’s standard
deviation is represented by error bars. “ns” = no significance, *p < 0.05, **p < 0.01, ***p < 0.001. Any comparisons lacking asterisks are non-significant. Scalebars = 100 μm

Villareal et al. Journal of Neuroinflammation(2024) 21:202

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Fig. 9 Proximity analysis of vasculature and astrocyte changes in COVID-AD. (A) Whole-tissue fluorescent scans of the hippocampus were taken using the
Olympus VS200 platform at 20x magnification. Scans were loaded into QuPath, and a script was run to generate 7 concentric rings, each with a radius of
10 μm, around all CD31-positive blood vessels. The 10 μm radius was chosen to be able to completely fit an astrocyte soma within the ring. (B) Representative 20x and 40x images of fluorescent double-staining for CD31 and GFAP (Pre-2020 N = 12, Non-COVID N = 12, and COVID N = 11). (C) % ROI coveredby CD31-positive vasculature, (D) subfield coverage by CD31, (E) GFAP-CD31 colocalization, and astrocyte-vessel proximity analysis were all performed in
the same section. Comparisons were performed using an ordinary one-way ANOVA with Tukey’s multiple comparisons test, with a single pooled varianceor an unpaired student’s t-test. Each group’s standard deviation is represented by error bars. “ns” = no significance, *p < 0.05, **p < 0.01, ***p < 0.001. Anycomparisons lacking asterisks are non-significant. Brightfield scale bars = 100 μm. Fluorescent scale bars = 60 μm and 20 μm, respectively

described below, suggesting that neuroimmune modulation could be both a primary effect of COVID-19
infection and upstream of any nascent accelerationsof AD pathogenesis. Notably, milder trends towards
increased Aβ burden were also observed in the more
CP-proximal regions and hippocampal subfields of
COVID patients in accordance with previous evidence that implicated the CP as a major conduit for
the transmission of systemic inflammation to the CNS[14, 29]. The pathological profiles of the Pre-2020 and
Non-COVID groups were similar and did not differ
significantly from one another, addressing the possibility of asymptomatic and undiagnosed patients existing
within the Non-COVID group by validating its representativeness against patients who could not havephysically been infected by SARS-CoV-2.

As the quintessential immune cells of the brain, we
first analyzed microglia using a variety of immunohistochemical markers to elucidate the precise neuroimmune profile of the COVID-AD brain. Systemic
infection survival was significantly associated withchronically elevated numbers of hippocampal GM
microglia, as evidenced by the quantification of Iba1positive cells. As a pan-microglial marker [51], theincreased amount of Iba1 positivity in COVID points
to heightened microgliosis and immune activation as
key characteristics of systemic-CNS immune communication, both of which have been previously demonstrated to occur in humans with AD and models of
the disease [15, 84, 85]. Importantly, we show that the
proliferation of microglia associated with COVID19 infection is additive to the proliferation alreadyVillareal et al. Journal of Neuroinflammation(2024) 21:202

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Fig. 10 COVID-19 infection is associated with region-specific dysregulation of oligodendrocyte and myelin-associated pathways in AD. Volcano plots
depicting DEGs in the hippocampus (A) and MFG (B) of COVID patients (N = 11) against a baseline of Non-COVID patients (N = 12) using the XT HS Glial
Profiling panel on the NanoString nCounter® platform. 2 × 20 μm-thick whole tissue FFPE sections – including combined subfields and cortex/whitematter - were used for RNA extraction. Bulk mRNA analysis was performed in the NanoString nSolver suite, with normalization of individual samples
using six housekeeping genes included in the glial profiling panel. Benjamini-Yekutieli p-value adjustment for multiple comparisons was performed for
differential expression. (C) Directed pathway scores in COVID hippocampus and MFG derived from the aforementioned nCounter® glial profiling panel
with positive, lighter bars indicating upregulation and negative, darker bars indicating downregulation. Pathways in both regions are compared against abaseline of Non-COVID and are arranged from most upregulated to most downregulated from top to bottom. Oligodendrocyte cell-type scores (specificto oligodendrocyte cells) (D) and markers (cell-type markers combined with some myelination-associated and functional genes) (E) derived from genes
included in the glial profiling panel are significantly lower (Fig. 12F) in COVID patients as compared to Non-COVID patients. (F) Representative hippocampal IHC stains of Olig2 and PLP1 used for QuPath quantification in COVID (N = 11) and Non-COVID (N = 12) patients. IHC was performed in 5 μm-thick FFPE
sections for both markers. Hippocampus Olig2 cells per mm2 (G) and PLP1 coverage (H) showed no differences at the whole region level but displayedsome loss of Olig2 positivity in COVID patients at the subfield level (Fig. 12L). IHC comparisons were performed using an unpaired student’s t-test. Eachgroup’s standard deviation is represented by error bars. “ns” = no significance, *p < 0.05, **p < 0.01, ***p < 0.001. Any comparisons lacking asterisks are nonsignificant. Scale bars = 100 μm

Villareal et al. Journal of Neuroinflammation(2024) 21:202

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Fig. 11 Quantification of CD3e-positive T-cells in the hippocampus CD3e-positive cells in the hippocampus were morphologically identical acrossgroups, with a small, spherical soma (A). Most CD3e-positive cells were observed within the vasculature; however, some parenchymal infiltration could
be observed, albeit in a limited manner. (B) Quantification of CD3e-positive T-cells in the hippocampus of COVID (N = 11), Non-COVID (N = 12), AND Pre2020 (N = 12) patients. (C) Subfield quantification of CD3e-positive cell coverage in the hippocampus. Subfield comparisons were made using an ordinary
two-way ANOVA with Tukey’s multiple comparisons test, with a single pooled variance. Each group’s standard deviation is represented by error bars. “ns”
= no significance, *p < 0.05, **p < 0.01, ***p < 0.001. Any comparisons lacking asterisks are non-significant. Scale bar = 60 μm

present within AD-afflicted, Non-COVID patients.
This refutes the possibility that because AD patientsare already at an advanced state of neurodegeneration and inflammation, any accessory inflammatory
insults would be essentially insignificant and negligible
in the advancement of the disease. Complementary tothis chronic elevation of proliferation, we observed
a reduction in microglial homeostasis in COVID
patients via quantification of P2RY12-positive cells. A
marker for resting and ramified microglia [51], loss of
P2RY12 positivity and morphological contraction ofprocesses are well-documented indicators of neuroimmune activation, with reductions in this subpopulationin the parenchyma being some of the earliest microglial changes observed in AD [17]. Though also present

in other brain regions, this shift from homeostatic toactivated microglia was most acute in the CP-adjacenthippocampus and the nearby FG + ITG cortices, providing further evidence of significant CP involvementin the transmission of inflammatory signals. Taken inconcert, inflammation from these individual diseases
is cumulative, resulting in microglial populations thatare less homoeostatic and more activated than those in
severe AD alone. Additionally, the described changes
in the neuroimmune axis occur despite a lack of
SARS-Cov-2 positivity at time of autopsy and with anindeterminate cytokine profile, suggestive of an undetermined, intriguing mechanism by which this microglial profile is chronically altered in COVID patients.

Villareal et al. Journal of Neuroinflammation(2024) 21:202

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Fig. 12 Bulk mRNA analysis of oligodendrocyte and myelin changes associated with COVID-AD. NanoString nCounter® Bulk mRNA data analysis ofwhole FFPE tissue sections from the hippocampus and MFG of COVID (N = 11) vs. Non-COVID (N = 12) patients. The top 25 DEGs based on significanceand their fold changes from hippocampus (A) and MFG (B) are listed from 1–25 with the most significant DEGs being located at the bottom of the graph.
Volcano plots for the DEGs making up the Oligodendrocyte Markers (C), Oligodendrocyte Differentiation/Maturation (D), and Myelogenesis (E) pathways
in the hippocampus with a baseline of Non-COVID. (F) p-values provided by nSolver for cell-type scoring validating oligodendrocyte scoring based oncell-type-specific DEG markers. (G-K) Univariate comparisons of selected DEGs. Adjusted p-values for individual genes can be found on Fig. 6 and a lackof stars does not indicate non-significance. Hippocampal subfield quantification of Olig2-positive cells per mm2 (L) and percent coverage of tissue by
PLP1 (M). Comparisons performed using an ordinary two-way ANOVA with Tukey’s multiple comparisons test, with a single pooled variance and *p < 0.05,**p < 0.01, ***p < 0.001. Each group’s standard deviation is represented by error bars. (N) Univariate comparisons of nCounter transcript counts for selectedcytokines. Generally, targets with visually lower counts such as CCL13, IL10, IL1B, 1L6, and TNF expressed too few transcripts for reliable comparisons to
be made. Bulk mRNA analysis was performed in the NanoString nSolver suite, with normalization of individual samples using six housekeeping genes
included in the glial profiling panel. Benjamini-Yekutieli p-value adjustment for multiple comparisons was performed for differential expression

Villareal et al. Journal of Neuroinflammation(2024) 21:202

Following the identification of increased microglial
activation in COVID patients, we sought out a morenuanced image of the functional manifestations of thisneuroimmune state. To do this, we evaluated various
markers of microglial activity in our cohort groups,
including CD68 and ferritin. No change in microglial phagocytosis and general reactivity as assessed by
CD68 immunopositivity [51, 86] was observed in the
COVID group, however, a trend towards elevated ferritin-positivity was present in the GM of all assessed
brain regions. Though subtle, this trend suggests that
COVID patients tend to have more dystrophic, ironaccumulating microglia [57, 59], possibly as a resultof chronic metabolic stress and ‘burnout’ [31, 87] or,more intriguingly, in response to increased levels of
periphery-originating free iron in the brain associated with COVID-19 infection and resultant sequelae[88]. The activation state of CNS microglia in COVID
patients was largely indeterminate, and further investigation is required concerning the functional characteristics of this population.
AD patients with COVID-19 also presented withextensive hippocampal astrocyte dysregulation ascompared to their Non-COVID counterparts exhibited by our quantifications of GFAP and ALDH1L1.
While literature concerning the presence and effectof GFAP-positive reactive astrogliosis in humans withAD remains largely incohesive, studies performed indisease models have shown that experimentally attenuating reactive astrocyte proliferation results in theadvancement of Aβ pathology [89], highlighting ourobservation of chronically reduced GFAP-positive
astrocytes in COVID patients as a potential avenue
for AD advancement. We also observed an attenuatedbut similar reduction in ALDH1L1-positive astrocytes,
suggestive of a global effect of systemic infection on
the astrocyte lineage rather than being limited to areactive subtype.
The indiscriminate nature of COVID astrocyte dysfunction and the cell type’s crucial role in the neurovascular unit [90] also imply some level of BBB dysfunctionin these patients, potentially amplifying communicationof systemic inflammation to the CNS. This is further supported by previous reports of BBB changes existing in
COVID-19 and AD separately [91, 92]. This hypothesis
is also in line with our ferritin-positive microglia data,
as destabilization in CNS iron homeostasis follows BBB
breakdown in multiple forms of disease [93]. Despite thisinformation, we did not observe focal astrocytic loss inproximity to blood vessels in COVID patients, which
could be indicative of other forms of BBB insult, such as
the dysregulation of gap junctions and increased permeability between CNS and periphery. Further investigationis necessary to better characterize if and to what extent

Page 23 of 27

the BBB plays a role in the changes observed in AD
patients suffering from systemic infection.
Interestingly, acute phenotypic shifts in astrocytes
were only observed in CP-adjacent regions such as the
hippocampus, which suggests that astrocyte dysfunction and/or loss in COVID patients is more associatedwith changes at the blood-cerebrospinal fluid barrier(BCSFB) rather than at the BBB and general vasculature
throughout the brain parenchyma. As we have observed
astrocytic loss to not be significantly tied to vessel and
BBB proximity, it is possible that the BCSFB is the major
effector in inflammatory signaling and dysregulation as
the other major CNS interface with the periphery. Further research is required to better distinguish hippocampal and CP-proximal astrocytes from their counterpartsin the MFG and other regions in COVID patients, andto understand the implications of these shifts on theneuroimmune response to COVID and AD. Aside fromvasculature support, CNS astrocytes have robust roles insynaptic maintenance and the perpetuation of the neuroimmune response [94], and more specific functional
characterization of COVID astrocytes should be performed in the future to understand their potential impact
on the progression of AD pathology.
Whole-tissue transcriptomic analysis of the same
patients generally confirmed the observations made in
our immunohistochemical studies but also revealed thepresence of extensive oligodendrocyte lineage (OL) dysregulation in COVID patients. WM and OL pathway
downregulation have been extensively documented in
COVID-19 patients and experimental models [95–97],
and the detection of these changes in AD patients highlights another infection-associated risk factor for diseaseprogression considering existing literature placing myelin
dysfunction as an early change in AD patients [73, 76, 77,98]. The lack of agreement in these changes at the protein level as assessed by the more broadly labeling markers OLIG2 and PLP1 could reflect a more particularizedalteration in these pathways, such as dysregulation ina subset of OL cells or in the lipid composition of the
myelin sheath instead of the lipid-protein profile. Since
we performed whole tissue RNA analysis, we did nothave the spatial resolution for these changes in expression to determine if they were preferentially localized tothe cortex or WM. Furthermore, many of the changes
observed in glia-related pathways in the hippocampal
subfields were localized to the directly periventricular
CA4-CA2 regions, implying that these regions receive
the highest concentration of inflammatory signals fromthe CP before diffusing to the rest of the hippocampus.
This was also supported by the degree of expression
changes between the MFG and the hippocampus, withthe latter experiencing more acute modulations of DEGs,
pathways, and overall tissue function.

Villareal et al. Journal of Neuroinflammation(2024) 21:202

Bulk RNA-seq also further validated patients in
the COVID group as being previously infected, withindividual genes upregulated in the group such as
TICAM1 and CTSB (Supp. Fig. 4I) having previously been demonstrated to be involved in COVID-19
pathogenesis [99–101].
Interestingly, the most conserved changes in the top
25 DEGs between regions were those associated withastrocytes such as ‘CHI3L1’ and ‘SERPINA3’, again suggesting the presence of CNS-wide BBB dysregulation
which is additive to blood-CSF barrier dysfunction in
the hippocampus resulting in differing degrees of neuroimmune modulation. CHI3L1 (also known as YKL40)
is of particular interest due to emerging evidence highlighting its role in neuroinflammatory astrocytic mechanisms, including the impairment of neurogenesis and thedevelopment of cognitive impairment [102, 103]. With
upregulation of the gene being observed both near andfar from the CP (albeit, at different intensities), it’s plausible that the astrocytic phenotype is associated with BBB
dysregulation and, with recent studies in animal models
tying its activity to Aβ burden [104], it could be an avenue for the advancement in Aβ pathology observed in
COVID-AD patients.
Here we demonstrate the ability for COVID-associated systemic inflammatory insults to be communicated to the CNS across multiple interfaces, inducing
a pro-inflammatory neuroimmune environment. In
patients already suffering from AD, this inflammation is cumulative, adding to pre-existing disruptionsof microglia and brain homeostasis associated withthe disease, potentially increasing the risk for disease advancement. Novel alterations in astrocyte
populations and oligodendrocyte function were also
observed, conveying the possible extent to whichperipherally located stimuli can influence the cellularcomposition of the CNS in AD and other neurodegenerative diseases.
While the self-report of COVID-19 infection history
by patients is a limitation of the study, we attempted toaddress this by adding comparisons with non-infectedpre-2020 patients, which validated our non-COVID
control group and allows for important insights into theinteraction between systemic infection and AD pathology
to be taken from this study. Previous studies performedby our group [20, 31] have investigated the microglial
response to Alzheimer’s disease pathology in comparison
to non-demented control patients. This study focused on
the effect of a COVID-19 infection on the neuroimmune
environment with concomitant Alzheimer’s disease
pathology. While this study would have benefitted from
including both COVID-negative and more importantly
COVID-positive non-demented control patient cohorts,
brains from donors with the latter condition were notPage 24 of 27

available in our brain bank at the time the study was conducted. It will be very important to continue to study theeffects of severe systemic infections, including COVID19 and more general sepsis on the neuroimmune environment in brains from neurologically normal donors
and donors suffering from neurodegenerative conditions.
Despite limitations in the power of our study, significant
changes in the CNS environment could still be detected,
and we encourage other centers to perform similar inquiries into COVID patients at their banks as further validation of these findings will only help to accelerate ourunderstanding of the interaction between these two diseases and could potentially inform future therapeuticstrategies.
Abbreviations
AD	Alzheimer’s diseaseAβ	Amyloid beta
NFT	Neurofibrillary tangles
CNS	Central nervous system
IHC	Immunohistochemistry
BBB	Blood-brain barrier
CP	Choroid plexus
ADNC	Alzheimer’s disease neuropathological changes
PMI	Post-mortem interval
FFPE	Formalin-fixed paraffin-embedded
CA	Cornus ammonis
MFG	Medial frontal gyrus
STG	Superior temporal gyrus
FG	Fusiform gyrus
ITG	Inferior temporal gyrus
WM	White matter
SUB	SubiculumEC	Entorhinal cortex
Iba1	Ionized calcium-binding adaptor molecule 1
P2RY12	Purinergic receptor P2Y12
CD68	Cluster of differentiation 68
GFAP	Glial fibrillary acidic protein
ALDH1L1	Aldehyde dehydrogenase 1 family member L1
DEG	Differently expressed genes
Olig2	Oligodendrocyte transcription factor 2
GAL3ST1	Galactose-3-O-sulfotransferase 1
PLP1	Proteolipid protein 1
TLR	Toll-like receptors
TICAM-1	TIR domain containing adaptor molecule 1
OL	Oligodendrocyte lineageBCSFB	Blood-cerebrospinal fluid barrier
Acknowledgements
We would like to thank Andrea Iturbe for useful discussions and comments on
the manuscript.Stefan Prokop is the Charlotte and Howard Zimmerman RisingStar Professor at the Fixel Institute for Neurological Diseases. The Olympus
VS200 scanner was purchased with a shared instrumentation grant from theoffice of the director at the National Institutes of Health (1S10OD032236).
Author contributions
JAB Villareal and S Prokop conceived the project, devised the experimental
design, performed experiments and data analysis, and drafted the manuscript.
T Bathe assisted with fine tuning cell detection scripts and provided
intellectual contributions. GP Hery performed experiments and provided
intellectual contributions, JL Phillips and W Tsering both contributed equallyand performed experiments.
Data availability
The datasets used and/or analyzed during the current study are available fromthe corresponding author on reasonable request.

Villareal et al. Journal of Neuroinflammation(2024) 21:202

Declarations
Competing interests
The authors declare no competing interests.
Received: 21 May 2024 / Accepted: 6 August 2024

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