Single-nuclei RNA sequencing uncovers heterogenous transcriptional signatures in Parkinson’s disease associated with nuclear receptor-related factor 1 defect

Abstract

Previous studies have found that deficiency in nuclear receptor-related factor 1 (Nurr1), which participates in the development, differentiation, survival, and degeneration of dopaminergic neurons, is associated with Parkinson’s disease, but the mechanism of action is perplexing. Here, we first ascertained the repercussion of knocking down Nurr1 by performing liquid chromatography coupled with tandem mass spectrometry. We found that 231 genes were highly expressed in dopaminergic neurons with Nurr1 deficiency, 14 of which were linked to the Parkinson’s disease pathway based on Kyoto Encyclopedia of Genes and Genomes analysis. To better understand how Nurr1 deficiency autonomously invokes the decline of dopaminergic neurons and elicits Parkinson’s disease symptoms, we performed single-nuclei RNA sequencing in a Nurr1 LV-shRNA mouse model. The results revealed cellular heterogeneity in the substantia nigra and a number of activated genes, the preponderance of which encode components of the major histocompatibility II complex. Cd74, H2-Ab1, H2-Aa, H2-Eb1, Lyz2, Mrc1, Slc6a3, Slc47a1, Ms4a4b, and Ptprc2 were the top 10 differentially expressed genes. Immunofluorescence staining showed that, after Nurr1 knockdown, the number of CD74-immunoreactive cells in mouse brain tissue was markedly increased. In addition, Cd74 expression was increased in a mouse model of Parkinson’s disease induced by treatment with 6-hydroxydopamine. Taken together, our results suggest that Nurr1 deficiency results in an increase in Cd74 expression, thereby leading to the destruction of dopaminergic neurons. These findings provide a potential therapeutic target for the treatment of Parkinson’s disease.

Introduction

Ten out of 107 individuals with hereditary Parkinson’s disease (PD) were reported to have two mutations in exon 1 of the nuclear receptor-related factor 1 (Nurr1) gene (Le et al., 2003). Nurr1 (also referred to as Nr4a2) is a member of the nuclear receptor superfamily and belongs to the Nr4a subgroup. Nur77 (Nr4a1) and Nor1 (Nr4a3) are the other two members of the Nr4a subgroup, and their functions appear to differ markedly from that of Nurr1 (Maxwell and Muscat, 2006; Chen et al., 2020b). Multiple studies have shown that Nurr1 is required for the formation, division, preservation, and longevity of midbrain dopaminergic neurons, which are most impaired in the brains of individuals with PD (Kadkhodaei et al., 2009; Decressac et al., 2013; Rodríguez-Traver et al., 2016).

Nurr1 appears to function as a transcriptional factor in the synthesis of classical markers of dopaminergic neurons, including tyrosine hydroxylase (TH), dopamine transporter (DAT), aromatic amino acid decarboxylase (AADC), and vesicular monoamine transporter (VMAT) (Jankovic et al., 2005; Kadkhodaei et al., 2009; Dong et al., 2016). The age-related decrease in Nurr1 expression in midbrain dopaminergic neurons has attracted researchers’ attention because it could explain the increased morbidity of PD in the elderly (Chu et al., 2002).

Furthermore, Nurr1 immunoreactivity is considerably lower in the substantia nigra of individuals who suffer from PD compared with healthy individuals (Balu et al., 2008). Multiple studies have shown that Nurr1 is expressed at lower levels in postmortem brain samples (Chu et al., 2006) and peripheral blood samples (Liu et al., 2012; Lou and Liao, 2012; Montarolo et al., 2016; Ruiz-Sánchez et al., 2017; Li et al., 2018; Yang et al., 2019) from patients with PD. In line with these clinical findings, genetic ablation of Nurr1 in mice results in a complete loss of dopaminergic neurons in the substantia nigra and ventral tegmental region, potentially leading to the death of newborn mice (Zetterström et al., 1997). Several dopaminergic neuron markers that are critical for the neurons of the nigrostriatal trajectory, such as TH, DAT, and AADC, are absent in Nurr1-deficient animals (Chu et al., 2006; Kadkhodaei et al., 2009). Moreover, heterozygous Nurr1-deficient animals exhibit greater vulnerability to the toxins 6-hydroxydopamine (6-OHDA) and 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (Le et al., 1999). Nurr1 conditional knockout mice mimic the early symptoms of PD, displaying a series of neuropathological changes such as a decrease in the number of striatal dopaminergic neurons and motor impairment, suggesting that they could be a useful model for studying PD (Kadkhodaei et al., 2013).

With recent technological advancements, single-cell/-nuclei RNA sequencing has emerged as a revolutionary technology that, in addition to cellular characterization (Papalexi and Satija, 2018; Abdelgawad et al., 2021) within specific microenvironments (Tikhonova et al., 2019; Gong et al., 2021), is critical in determining disease mechanisms (Chen et al., 2020c; Kim et al., 2021) as well as in drug discovery (Wu et al., 2017; Chen et al., 2020a) and drug repurposing (Xu et al., 2021). Here, we employed single-nuclei RNA sequencing to investigate how Nurr1 deficiency alone leads to dopaminergic neuron degeneration in PD.

Methods

Cell culture and Nurr1_shRNA lentiviral transient transfection

The immortalized dopaminergic MES23.5 cell line (RRID: CVCL_J351, Chinese Academy of Sciences, Shanghai, China), authenticated by short tandem repeat loci, was maintained at a density of 5 × 10⁴ cells/mL in 25 cm² dishes (Corning, Acton, MA, USA) pre-coated with 25 mg/mL poly-D-lysine. In an incubator with 5% CO₂ at 37°C, cells were grown in Dulbecco’s modified Eagle medium/Nutrient Mixture F-12 (DMEM/F12) containing 10% fetal bovine serum (Atlas, Fort Collins, CO, USA) and 1% penicillin (Invitrogen, Carlsbad, CA, USA). Cells were grown to a minimum of 80% confluency before being passaged and were passaged 10 to 15 times to reach full maturity before being used in subsequent experiments. The mature cells were plated at a density of 5 × 10⁴ cells/cm² on poly-L-lysine-coated six-well plates 24 hours before lentiviral transient transfection and then switched to serum-free DMEM/F12 (Gibco, Shanghai, China) overnight. Lipofectamine 2000 (Invitrogen) was used to carry out the transfections following the manufacturer’s instructions. Three different plasmids (LV-Nr4a2-mus-526, LV-Nr4a2-mus-667, and LV-Nr4a2-mus-787) (Genepharma, Suzhou, China) were used, and the one with the greatest knockdown effect was used for further experiments. Additional file 1 ^{(735.7KB, pdf)} explains how the transfection was conducted; for the sake of simplicity, only the most effective knockdown plasmid (Genepharma) is shown. Briefly, after cells were grown in a six-well plate, they were fasted overnight by switching to serum-free DMEM/F12 (2 mL in each well). Subsequently, the lentivirus (50 μL) was added to every 1 mL of serum-free medium, according to the respective groups. This was followed by an immediate addition of lipofectamine (5 μL). The proportion of cells that exhibited green fluorescence as detected by a fluorescence microscope (BX51, Olympus, Tokyo, Japan) 72 hours after transfection was 90%. To screen for successfully transfected cells, the transfection mixture was removed and replaced with a fresh complete medium containing 8 μg/mL puromycin (ThermoFisher Scientific, Shanghai, China). Transfected cells were subsequently grown in puromycin-containing medium to maintain the plasmid.

Quantitative polymerase chain reaction

Briefly, each dish of cells was washed with phosphate-buffered saline (PBS, Dalian Meilun Biotechnology, Dalian, China), and 1 mL of TRIzol was added (Ambion-Life Science Technologies, Shanghai, China). Then, quantitative polymerase chain reaction was performed as we described previously (Kambey et al., 2021b; Additional file 1 ^{(735.7KB, pdf)} ). The primers used were NR4A2 forward: 5′-GTG TTC AGG CGC AGT ATG G-3′, reverse: 5′-TGT ATT CTC CCG AAG AGT GGT AA-3′, and β-actin forward: 5′-ACG GCC AGG TCA TCA CTA TTG-3′, reverse: 5′-CAA GAA GGA AGG CTG GAA AAG A-3′.

Mass spectrometry

Immunoprecipitation

To determine the effects of Nurr1 knockdown on neuron proteomics, we cultured MES23.5 cells and then divided them into two groups (Nurr1 LV_shRNA and Nurr1 LV_Scramble), with three replicates each. Samples were gathered, and protein extraction was carried out as described earlier (Kaboord and Perr, 2008). Because the level of Nurr1 enrichment in dopaminergic neurons appeared to be modest, we opted to undertake immunoprecipitation using magnetic beads (Medchemexpress, Shanghai, China) prior to performing tandem mass spectrometry (MS). Total protein was isolated from the samples and immunoprecipitated with a Nurr1 antibody (rabbit, 1:200, Abcam, Shanghai, China, Cat# ab41917, RRID: AB_776887) before trypsin digestion and processing for label-free quantification tandem MS, which are described in detail below.

Trypsin digestion

After washing the samples repeatedly with 200 μL of 1× PBS and centrifuging them at 12,000 × g for 10 minutes at 4°C, the supernatant was discarded. The samples were then resuspended in 100 μL of 50 mM NH₄HCO₃ (Tianjin Chengyuan Chemical, Tianjin, China). Disulfide bonds were reduced by adding dithiothreitol (Chongqing Borning Chemical & Industrial, Chongqing, China) to a final concentration of 10 mM, and the mixture was incubated at 56°C for 1 hour. Iodoacetamide solution (Sigma, Shanghai, China) was added to final concentration of 50 mM, and the reaction was allowed to proceed for 40 minutes. Trypsin (Sigma, Shanghai, China) was added to the protein substrate at a mass ratio of 1:100, and the reaction was allowed to proceed at 37°C for 4 hours. Then, trypsin was added once more at a mass ratio of 1:100, and the mixture was digested for 16 hours at 37°C. The peptide was desalted using a self-filling desalting column after enzymatic digestion and dried in a vacuum centrifuge concentrator at 45°C. After dissolving the peptides in sample solution containing 0.1% formic acid and 2% acetonitrile (Sigma, Shanghai, China), the mixture was centrifuged at 4°C for 10 minutes with a full oscillating vortex at 13,200 × g. The supernatants were then transferred to fresh sample tubes for MS analysis.

Label-free liquid chromatography coupled with tandem MS (LC-MS/MS)

High-pH reverse performance liquid chromatography was used to separate the peptides on an Agilent 300 Extend C18 column (Agilent Technologies, Santa Clara, CA, USA) with a particle size of 5 μm, an internal diameter of 4.6 mm, and a length of 250 mm. The process involved the following steps: At a pH value of 9, the peptide gradient ranged from 8% to 32% acetonitrile. Sixty components were separated over 60 minutes; the peptides were then concatenated into 14 components, which were dehydrated using a vacuum dryer to prepare for the following steps. After being dispersed in 0.1% formic acid (solvent A), the tryptic peptides were deposited directly onto a pre-column phase and then separated using a reversed-phase analytical column (Acclaim PepMap RSLC, Thermofisher Scientific, Waltham, MA, USA). The gradient consisted of a 26-minute increase from 6% to 23% solvent B (0.1% formic acid in 98% acetonitrile, Thermofisher Scientific, Waltham, MA, USA), an 8-minute increase from 23% to 35%, a 3-minute climb to 80%, and a final 3-minute hold at 80% (on an EASY-nLC 1000 ultra-performance liquid chromatography system at a constant flow rate of 500 nL/min). The peptides were subjected to nanospray ionization followed by ultra-performance liquid chromatography tandem mass spectrometry (MS/MS) using an internet-connected Q ExactiveTM Plus (Thermofisher Scientific, Waltham, MA, USA). The electrospray voltage used was 2.0 kV. For a comprehensive scan, the m/z range used was 350 to 1800, and whole peptides were identified at a resolution of 70,000 using an Orbitrap mass spectrometer (Thermofisher Scientific, Waltham, MA, USA). After the peptides were selected for MS/MS with a normalized collision energy value of 28, the fragments were identified by the Orbitrap spectrometer at a resolution of 17,500. A data-dependent approach in which one MS scan was followed by 20 MS/MS scans with 15.0-second dynamic exclusion was used. Automatic gain control was set at 5E4, and 100 m/z was assigned to the fixed initial mass.

LC-MS/MS data analysis

The MaxQuant search engine v1.5.2.8 (Max Planck Institute, Berlin, Germany) was used to process and analyze the data. The UniProt database (UniProt Consortium, 2021; Wang et al., 2021) was combined with a reverse decoy database to analyze the tandem MS/MS spectra. The other parameters were set and the final analysis was performed as described previously (Yu et al., 2021). The Clue gene ontology (ClueGO) plug-in for Cytoscape (Cytoscape Consortium, San Diego, CA, USA) was used to identify differentially expressed genes that may be associated with PD.

Animals

Owing to hormonal influences and the low body weight of female mice (Fillingim and Ness, 2000; Craft et al., 2004; Mogil and Chanda, 2005), only male mice were used in this study. Sixty-five C57BL/6J male mice aged 8 weeks, weighing 25–30 g, were supplied by the Animal Center of Xuzhou Medical University (license No. SYXK [Su] 2020-0048). For the first phase of the animal experiments, mice were divided into two groups: Nurr1 knockdown (Nurr1 LV_shRNA, n = 30) and control (Nurr1 LV_Scramble, n = 20). In the second phase of the animal experiments (n = 15), Nurr1 LV_shRNA was injected into the left side of the striatum (0.5 mm anterior- posterior, 1.5 mm lateral, 3.5 mm ventral to the dural surface), while Nurr1 LV_Scramble was injected into the right side as the control (same brain coordinates). Animals (five mice in each cage) were provided with consistent meals and free access to water and maintained under a 12/12-hour light/dark cycle with controlled temperature (15–28°C), and humidity (40–65%) conditions. Animals were treated in accordance with the rules set by the Ethical Committee for the Use of Research Animals of Xuzhou Medical University (approval No. L20210226270, June 16, 2021). All experiments were designed and reported according to the Animal Research: Reporting of In Vivo Experiments (ARRIVE) guidelines (Percie du Sert et al., 2020).

Stereotaxic injection of Nurr1 LV_shRNA

Nurr1 LV_shRNA viral stock comprising 1 × 10⁶ infectious viral units in a 200 μL volume was supplied by Santa Cruz Biotechnology (Dallas, TX, USA). These lentiviral particles, intended for use in gene silencing, were delivered as transduction-ready viral particles. The particles contained three to five expression constructs comprising a target-specific 19–25 nucleotide sequences plus hairpin yielding an shRNA designed to silence gene expression. Additional Figure 1 ^{(77.9KB, pdf)} shows how the Nurr1_shRNA plasmid was produced. After general anesthesia was induced by intraperitoneal injection of 45 mg/kg sodium pentobarbital (Sigma, St. Louis, MO, USA), mice were positioned within a stereotaxic apparatus (RWD, San Diego, CA, USA), and the bite bar was adjusted to a distance of 0 mm. After exposing the skull, a high-speed dental drill was used to create the burr hole. Twenty animals were injected with 7 μL of Nurr1 LV_shRNA in the left striatum (0.5 mm anterior- posterior, 1.5 mm lateral, 3.5 mm ventral to the dural surface (Earls et al., 2019). The control group (20 mice) received 7 μL of Nurr1 LV_Scramble (Santa Cruz Biotechnology, Dallas, TX, USA). The injection was delivered over 4 minutes using a Hamilton micro syringe with a capacity of 10 μL. The needle was then left in place for an additional 4 minutes, as described in a prior study (Aoi et al., 2000). TH immunoreactivity was used to confirm the degeneration of dopamine neurons after 4 weeks. To validate the activated genes after single nuclei-RNA sequencing, a second phase of lentiviral infusion (Nurr1 LV_shRNA and Nurr1 LV_Scramble, n = 10) was performed, with the contralateral striatum used as a control.

Validation of activated genes identified by single-nuclei RNA sequencing in the PD mouse model

We established a PD mouse model as we described previously (Kambey et al., 2021b). In brief, 6-OHDA (Medchemexpress) was prepared (5 μg in 1 μL of 0.9% normal saline containing 0.2% ascorbic acid). Then, 2 μL of 6-OHDA was injected into the left striatum, and 2 μL of normal saline was injected into the right striatum of the same mouse to serve as a control (n = 15). The same brain coordinates were used for each injection, as described in more detail above. Four weeks later, quantitative polymerase chain reaction (qPCR) was performed. Briefly, the midbrain was extracted on ice and rapidly homogenized in 1 mL of TRIzol. The subsequent steps are shown in Additional file 1 ^{(735.7KB, pdf)} . The delta-delta Ct technique was used to analyze the results, and glyceraldehyde-3-phosphate dehydrogenase (Gapdh) mRNA was used as an internal reference. The following primers were used as: Gapdh, forward: 5′-CGG GGT CCC AGC TTA GGT TC-3′, reverse: 5′-GCC CAA TAC GGC CAA ATC CG-3′; Cd74, forward: 5′-AGT GCG ACG AGA ACG GTA AC-3′, reverse: 5′-CGT TGG GGA ACA CAC ACC A-3′.

Behavioral testing

Open field test

In the first week after stereotaxic surgery, six mice died (five from the Nurr1 knockdown group and one from the control group). The remaining animals (n = 34) were separated into two groups (control versus experimental) prior to performing the behavioral tests. To assess locomotion (in the mice that only received lentiviral infusion, in phase one of the animal experiments), the open field test was performed 4 weeks post-stereotaxic surgery. Locomotion analysis is one of the most well-established tests in rodents and is also the most standardized method for measuring motor function (Schwarting et al., 1993; Zhang et al., 2020). It can be carried out in an open or closed field. Mice were placed in the center of a 50 cm × 50 cm enclosure with 50 cm-high walls and given 10 minutes to explore. The center section was lit at 300 lx, and the amount of time spent in the central zone over 30 minutes was recorded. Having all four paws in the middle area was classified as entry into the central zone. The ANY-maze video-tracking system (Muromachi Kikai, Tokyo, Japan) was used to digitally record and analyze the animals’ behavior.

Elevated body swing test

Three weeks after lentiviral infusion/stereotaxic surgery (only for phase one of the animal experiments), the elevated body swing test was performed for both groups (Nurr1 knockdown and control). The same test was repeated in the fourth and fifth weeks. The elevated body swing test is also referred to as the tail suspension test (Can et al., 2012), especially in studies conducted in mice to investigate antidepressants, in which immobility time rather than swing path direction is assessed. The same mice that were assessed in the open field test were subjected to the elevated body swing test. The elevated body swing test was carried out according to Borlongan & Sanberg’s original description (Borlongan and Sanberg, 1995) with minimal modifications. Each mouse was placed in a transparent cage measuring 36 cm × 19.5 cm × 18.5 cm and permitted to choose a comfortable position with all four of its paws on the ground. It was then elevated to around 10 cm from the bottom of the cage by the tip of the tail, and the direction of the first body swing was recorded. A body swing was defined as bending the upper body away from the vertical axis by more than 10° to either side. The animal was then replaced on the ground and allowed to adjust its position in preparation for the subsequent swing test. Each mouse was re-suspended once it was clearly balanced and showed no predilection for one side or the other. The test was carried out 20 times for each animal. If the animal did not start swinging, the base of its tail was gently tapped to encourage it to do so. To prevent affecting the direction of swings, the assessor alternated the hand used to pick up the mouse and where he stood in relation to the testing cage. Additionally, the test was carried out in an area that was free of any objects that could influence swing direction. The fraction of swings toward the right side was calculated. The number of initial head or upper body turns contralateral to the lesioned side was determined and standardized over 20 consecutive trials as follows: percentage of contralateral recovery = [1 – (lateral turning in 20 trials – 10)/10] × 100 (Borlongan and Sanberg, 1995; Henderson et al., 1999; Chang et al., 2003).

Western blot assay

Four weeks post-lentiviral infusion, western blotting was performed for dopaminergic neuron markers. First, 300 μL of a lysis buffer cocktail comprising radioimmunoprecipitation assay buffer (Medchemexpress) and phenylmethanesulfonyl fluoride inhibitor (Medchemexpress) in a 100:1 ratio was prepared and added to ice-cold cells or midbrain samples. The samples were homogenized for 30 minutes with vortexing every 5 minutes. The homogenates were then centrifuged at 4°C at 14,000 × g for 30 minutes. The supernatants were transferred to fresh 0.5 mL tubes and maintained on ice, and the pellets were discarded. A bicinchoninic acid kit (Medchemexpress) was used to determine the protein concentrations of the samples following the manufacturer’s instructions. The optical densities of the protein standards and samples were calculated. The protein concentrations of the samples were standardized using radioimmunoprecipitation assay buffer, and the samples were then boiled in loading buffer (a reducing agent) at 100°C for 10 minutes. Next, 20-μg protein samples were subjected to electrophoresis on a 10% polyacrylamide gel at a constant voltage of 80 V while the proteins migrated through the stacking gel, at which point the voltage was increased to 100 or 120 V for separation of the protein bands. The proteins were then electrotransferred (100 V, 1 hour) to nitrocellulose membranes. The membranes for 2 hours in Tris-buffered saline before incubation with primary antibodies to TH (rabbit, 1:500, Proteintech, Shanghai, China, Cat# 25859-1-AP, RRID: AB_2716568), DAT (mouse, 1:500, Novus Biologicals, Shanghai, China, Cat# H00006531-A01, RRID:AB_670572), glial cell line-derived neurotrophic factor (GDNF) (rabbit, 1:1000, Abcam, Cat# ab18956, RRID:AB_2111379), Nurr1/NR4A2 (rabbit, 1:500, Proteintech, Cat# 10975-2-AP, RRID:AB_2153760), and β-actin (mouse, 1:2000, Santa Cruz Biotechnology, Shanghai, China, Cat# sc-47778 HRP, RRID:AB_2714189) overnight at 4°C. The membranes were then incubated for 6 hours at room temperature (25–32°C) and subsequently probed with appropriate secondary antibodies (goat anti-rabbit IgG H&L (HRP), 1:200, Abcam, Cat# ab6721, RRID: AB_955447, and goat anti-mouse IgG H&L (HRP), 1:200, Abcam, Cat# ab6789, RRID: AB_955439). Following the blotting process, the membranes were examined using a scanner (LI-COR Biotechnology, Lincoln, NE, USA) and an image analyzer (LI-COR Biotechnology, Lincoln, Nebraska, USA). The optical density of bands relative to β-actin bands was analyzed with ImageJ software (v1.52a, National Institute of Health, Bethesda, MD, USA) (Schneider et al., 2012).

Immunohistochemistry

Four weeks post-lentiviral infusion, immunohistochemistry was conducted as we described previously (Kambey et al., 2021b). The detailed protocol is provided in Additional file 1 ^{(735.7KB, pdf)} .

Immunofluorescence staining

Four weeks post-lentiviral infusion, immunofluorescence staining was performed. Following pentobarbital (45 mg/kg) anesthesia and cardiac perfusion (with 100 mL of PBS followed by 150 mL of cold 4% paraformaldehyde), the brains were harvested and post-fixed in 4% paraformaldehyde for an additional day before being cryoprotected in a solution of 20% (for 24 hours) and 30% (for 24 hours) sucrose in 0.1 M phosphate buffer at 4°C. Coronal brain sections (including the substantia nigra and other midbrain areas) were made at a thickness of 16 μm and air-dried overnight. The sections were then blocked for 2 hours at room temperature with 0.1% Triton X-100 and 10% normal goat serum diluted in 0.1 M phosphate buffer. TH and Nurr1 were detected by incubating sections with TH antibody (rabbit, 1:500, Proteintech, Cat# 25859-1-AP, RRID: AB_2716568 or mouse, 1:400, Santa Cruz Biotechnology, Dallas, TX, USA, Cat# SC-25269, RRID: AB_628422), and Nurr1/NR4A2 polyclonal antibody (rabbit, 1:500, Proteintech, Cat# 10975-2-AP, RRID: AB_2153760) overnight at 4°C. After being thoroughly washed, the stained sections were incubated for 6 hours at room temperature with Alexa Fluor 488-conjugated AffiniPure goat anti-rabbit IgG (1:200, Jackson ImmunoResearch, Carlsbad, CA, USA, Cat# 111-545-003, RRID: AB_2338046). For double labeling for TH and CD74 or Nurr1 and CD74, midbrain sections were incubated overnight at 4°C with antibodies to TH (1:500) and CD74 (mouse, 1:500, Proteintech, Cat# 66390-1-Ig, RRID: AB_2881766) or Nurr1 (1:500) and CD74 (1:500). The slices were then probed for 6 hours at room temperature with Alexa Fluor 488-conjugated AffiniPure goat anti-rabbit IgG (1:200, Jackson ImmunoResearch Carlsbad, CA, USA, Cat# 111-545-003, RRID: AB_2338046) and Alexa Fluor 594-conjugated AffiniPure goat anti-mouse IgG (1:200, Jackson ImmunoResearch Carlsbad, CA, USA, Cat# 128-585-003, RRID: AB_2783778). 4′,6-Diamidino-2-phenylindole (DAPI) nuclear counterstain was applied for 15 minutes. The slides were mounted and examined under a fluorescence microscope. The immunoreactivity relative to the counterstain (DAPI) was assessed and reported.

Single-nuclei RNA sequencing

Nuclear isolation

Four weeks post-lentiviral infusion, the midbrain was harvested from five mice in each group for nuclear isolation. Nuclei were isolated using a slightly revised 10× Genomics® protocol (10× Genomics, Shanghai, China). Briefly, cold lysis buffer containing 10 mM Tris-HCl, 10 mM sodium chloride, 3 mM MgCl₂ and 0.1% Nonidet P40 (10× Genomics, Shanghai, China) was used to break up the tissues. Then, the suspension was filtered and centrifuged, and the supernatant was discarded. The pellets containing the nuclei were washed in “nuclei wash and resuspension buffer” containing 1× PBS, 1% bovine serum albumin, and 0.2 U/μL RNase inhibitor (10× Genomics, Shanghai, China), then filtered and centrifuged a second time. To isolate the nuclei, the pellets were first suspended in DAPI solution for 10 minutes. Then, a FACS Diva Cell Sorter (BD Biosciences, San Jose, CA, USA) was used to isolate single nuclei that were positive for DAPI.

Library generation and sequencing

A Chromium Next GEM Single Cell 3’ Kit v3.1 (10× Genomics, Pleasanton, CA, USA) was used to construct complementary DNA libraries from isolated nuclei. An Agilent 2100 Bioanalyzer System (Agilent Technologies) was used to assess complementary DNA quality, and sequencing was performed on an Illumina NovaSeq 6000-S2 apparatus (Illumina, San Diego, CA, USA).

Transcript quantification, filtration, and processing

To produce FASTQ files from the raw base call outputs, the mkfastq pipeline in Cell Ranger (10× Genomics, Pleasanton, USA) was used. Cell Ranger count pipeline v.3.0 (10× Genomics, Pleasanton, USA) was used to build a gene-barcode unique molecular identifier (UMI) count matrix for each sample. We only included genes in the analysis that had at least one UMI count, and cells with fewer than 400 genes or with a mitochondrial UMI count greater than 10% were excluded. Ribosomal and mitochondrial genes were excluded. The processed UMI feature-barcode matrices constructed with Cell Ranger v2.3.4 (10× Genomics, Pleasanton, USA) were analyzed with the Seurat package using R software (Robert Gentleman and Ross Ihaka, Boston, MA, USA) (Satija et al., 2015; Hao et al., 2021).

Normalization, sample integration, and cell clustering

Samples were standardized using the “LogNormalize” technique (Booeshaghi and Pachter, 2021; Seth et al., 2022) to reduce cell-specific biases prior to downstream analyses. To perform multi-sample data integration, we used the weighted-nearest neighbor approach (Hao et al., 2021). This is the Satija task force’s most recent single-cell sequencing data integration technique for combining numerous single-cell samples. Using the Louvain algorithm, the normalized data were aggregated. UMAP (Uniform Manifold Approximation and Projection), a new fluid learning technique for dimensional reduction that consists of theoretical frameworks based on Riemann geometry and algebraic topology, was used to depict the cell clustering results. Using Seurat’s FindAllMarkers tool (https://www.rdocumentation.org/packages/Seurat/versions/4.0.5/topics/FindAllMarkers), we identified marker genes for each cluster.

SingleR (https://www.bioconductor.org/packages/release/bioc/html/SingleR.html), which is a novel computational technique for unbiased single-cell ribonucleic acid sequencing (scRNA-seq) cell type recognition that uses reference transcriptome datasets of pure cell types to determine the cell from which each individual cell originated, was used to annotate cell types (Mabbott et al., 2013; Grün et al., 2016; La Manno et al., 2016; Aran et al., 2019). Even when automatic cell annotation is employed, it is frequently necessary to perform manual annotation due to the inconsistencies that exist in the marker datasets and single-cell atlases that automated techniques rely on (Clarke et al., 2021). Thus, we supplemented the automated annotation with known cell markers based on the Labome (Tanapat, 2013), CellMarker (Zhang et al., 2019), and PanglaoDB (Franzén et al., 2019) databases.

Differential gene expression

A negative binomial test with a false discovery rate adjusted P-value of < 0.05 was performed in Seurat 4.0 (Rahul Satija, Lower Manhattan, NY, USA) to identify genes with a log fold enrichment of 0.25 that were found in at least 25% of the cluster’s cells. Briefly, the following parameters were taken into account: the proportion of genes expressed in the cell population, pct.1 or pct.2 > 0.25; expression multiple, avg log2FC > 0.25; and P val adj < 0.01, where pct.1 or pct.2 denotes the percentage of cells in which the gene was found in the first or second group, respectively. In this study, the Nurr1 LV_shRNA group and control group clusters were determined by differential marker gene expression analysis (FindAllMarkers feature in Seurat), and differentially expressed genes between each cluster were then identified.

Functional analysis

Using Singleseqgset (https://github.com/arc85/singleseqgset) in Seurat, the differentially expressed genes were analyzed for gene ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, and Reactome enrichment. The stratified GO layout was visualized with directed acyclic graphs (Aranguren et al., 2007; Textor et al., 2016). Singleseqgset is a package used to perform scRNA-seq data gene set enrichment analysis by applying a fundamental underlying statistics to calculate the enrichment of gene sets of interest across clusters (Zou et al., 2021).

Statistical analysis

Although sample sizes were not predetermined using statistical methods, our sample sizes are comparable to those reported in earlier publications (Zhang et al., 2022; Zhou et al., 2022). The analysis did not omit any animals or data points, and the assessors were not aware of the group assignments. GraphPad Prism version 9.0.0 for Windows (GraphPad Software, San Diego, CA, USA, www.graphpad.com) was employed for statistical analysis. Tukey’s multiple comparisons test preceded by one-way analysis of variance was used to compare multiple groups, whereas Student’s t-test was used for comparisons between two groups. The data are presented as the mean ± standard deviation (SD). P < 0.05 was deemed statistically significant.

Results

Nurr1 knockdown reduces TH expression

TH is a well-known dopaminergic neuron marker (Mendez et al., 2005; Tian et al., 2008). To determine whether Nurr1 is necessary for dopaminergic neuron survival, TH expression was analyzed after Nurr1 knockdown. First, we sought to identify the plasmid that conferred the greatest level of knockdown. MES23.5 cells were transiently transfected with the three plasmids using Lipofectamine™ 2000. Fluorescence was evident after 72 hours, indicating that the transfection was successful. Additional file 1 ^{(735.7KB, pdf)} explains how the transfection was conducted. For the sake of simplicity, only the most effective knockdown plasmid is shown. The knockdown efficiency of the plasmids was also determined by qPCR, which showed that LV-Nr4a2-mus-526 conferred the greatest level of knockdown (Figure 1A and B). The western blot results indicated that Nurr1 expression was significantly lower in cells transfected with LV-Nr4a2-mus-526 compared with the control (P = 0.0487, t = 4.366, df = 2; Figure 1C and D). TH expression was markedly lower in Nurr1 knockdown cells compared with control cells (P = 0.0021, t = 21.95, df = 2; Figure 1E and F).

Figure 1.

Nurr1 knockdown reduces TH expression.

(A) Nurr1 (Nr4a2) knockdown in a MES23.5 cells as assessed by fluorescence microscopy (original magnification 4×, scale bars: 200 μm). The green fluorescence indicates Nurr1 expression. (B) The plasmid that induced the greatest degree of knockdown was determined by quantitative polymerase chain reaction. (C, D) Nurr1 expression was significantly reduced in the knockdown cells compared with the control group. (E, F) Tyrosine hydroxylase expression was dramatically reduced after Nurr1 knockdown. The data are presented as the mean ± SD (n = 4). *P < 0.05, **P < 0.01 (one-way analysis of variance followed by Tukey’s multiple comparisons). LV-NC: Lentiviral-normal control; LV-Nr4a2-mus: lentiviral, Nr4a2 promoter, mouse; TH: tyrosine hydroxylase.

LC-MS/MS reveals up-regulated genes linked with PD

To identify changes in protein expression associated with Nurr1 knockdown and to determine whether these changes are associated with PD, label-free quantitative LC-MS/MS proteomics analysis was carried out. A log2-transformed fold change (log2FC) of 1 or –1 and a false discovery rate value of 0.05 were used to define differentially expressed proteins. We found that 231 genes were upregulated, but no genes were downregulated, in Nurr1 knockdown cells compared with the control group (Additional Table 1 ^{(376.4KB, pdf)} ). Using ClueGO, a Cytoscape plug-in that generates functionally structured GO category networks by combining GO terms with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, we identified 14 differentially expressed genes (Ndufa9, Ndufa4, Ndufa2, Psmd12, Gnai3, Tubb4b, Sdhb, Psma6, Psmc3, Ndufs3, Uqcrfs1, Vdac1, Ndufv1, and Slc25a4) that were linked to the PD pathway (Figure 2). The majority of these genes are involved in key biological processes (Additional Figure 2 ^{(118.1KB, pdf)} ).

Figure 2.

Immunoprecipitation followed by label-free quantitative LC-MS/MS proteomics analysis identified up-regulated genes linked with Parkinson’s disease.

(A) KEGG pathway analysis results showing various genes that are linked to Parkinson’s disease. (B) The names of the genes, as well as their P- and Q-values. The blue box highlights those associated with Parkinson’s disease. (C) ClueGO analysis revealed a Parkinson’s disease node. Nodes of the same color indicate related clusters. ClueGO: Clue Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; LC-MS/MS: liquid chromatography coupled with tandem mass spectrometry.

Pathological effects of Nurr1 knockdown in a mouse model

Next, we first performed in vivo behavioral analyses to determine whether Nurr1 knockdown recapitulates behavioral features of PD. The results indicated that there was a marked difference between the Nurr1 knockdown and control groups in the open field test. Unlike the control group, the Nurr1 knockdown group showed a significant difference between the number of clockwise and anticlockwise rotations (P = 0.0041, t = 3.287, df = 18; Figure 3A and B). In addition, animals in the Nurr1 knockdown group crossed the line fewer times than animals in the control group (P = 0.0006, t = 4.140, df = 18; Figure 3A and C). The elevated body swing test demonstrated a significantly lower degree of contralateral turning recovery in the Nurr1 knockdown group compared with the control group (P = 0.0001, t = 22.31, df = 12; Figure 3D).

Figure 3.

Behavioral analysis revealed marked alteration of locomotion and contralateral turning in Nurr1 knockdown mice relative to the control group.

(A) The two groups were compared in an open field test. (B) Mice in the Nurr1 knockdown group turned primarily to the side with the most dopaminergic neurons. (C) Analysis of line crossings in the open field test revealed a significant difference between the two groups. (D) In the elevated body swing test there was a marked decrease in the proportion of contralateral turning recovery in the Nurr1 knockdown group compared with the control group. The data are presented as the mean ± SD (n = 12). **P < 0.01, ***P < 0.001 (Student’s t-test). Nurr1 LV_Scramble: Nurr1 Lentiviral_Scramble; Nurr1 LV_shRNA: Nurr1 Lentiviral_short hairpin RNA.

The methods used to assess TH and Nurr1 immunoreactivity in the striatum and perform double labeling of TH and Nurr1 in the substantia nigra are described in Additional file 1 ^{(735.7KB, pdf)} . The immunofluorescence staining results showed a noticeable decrease in Nurr1⁺ and TH⁺ neurons in the substantia nigra, as indicated in Figure 4A and B, respectively, suggesting that Nurr1 knockdown leads to dopaminergic neurons degeneration.

Figure 4.

Nurr1 (A) and tyrosine hydroxylase (B) immunoreactivity.

There was a noticeable decrease in the number of Nurr1⁺ and TH⁺ neurons in the substantia nigra of the Nurr1 knockdown group relative to the control group. Scale bars: 200 μm (upper), 100 μm (lower). Nurr1: Nuclear receptor-related factor 1; Nurr1 LV_Scramble: Nurr1 Lentiviral_Scramble; Nurr1 LV_shRNA: Nurr1 Lentiviral_short hairpin RNA; TH: tyrosine hydroxylase.

The decline in dopaminergic neuron numbers was assessed by western blotting of midbrain samples using dopaminergic neuron markers (TH and DAT). GDNF expression was also ascertained because it is regulated by Nurr1 (Decressac et al., 2012). In comparison to the control group, there was a significant reduction in the expression of these proteins in the Nurr1 knockdown group [TH (P = 0.0275, t = 3.392, df = 4), DAT (P = 0.0059, t = 5.336, df = 4), Nurr1 (P = 0.0344, t = 3.153, df = 4), GDNF (P = 0.0119, t = 4.373, df = 4)] (Figure 5A–E).

Figure 5.

Western blot assay reveals a decline in dopaminergic neuron marker expression in the Nurr1 knockdown group compared with the control group.

(A) Immunoblotting for Nurr1, GDNF, and two markers of dopaminergic neurons (TH and DAT). (B–E) Quantitative analysis. The data are presented as a mean ± SD (n = 3). *P < 0.05, **P < 0.01 (Student’s t-test). DAT: Dopamine transporter; GDNF: glial cell line-derived neurotrophic factor; Nurr1: nuclear receptor-related factor 1; Nurr1 LV_Scramble: Nurr1 Lentiviral_Scramble; Nurr1 LV_shRNA: Nurr1 Lentiviral_short hairpin RNA; TH: tyrosine hydroxylase.

Cellular heterogeneity in the substantia nigra following Nurr1 knockdown

To better understand how the Nurr1 deficiency alone causes a decline in dopaminergic neuron numbers and elicits PD-like symptoms, we performed single-nuclei RNA sequencing. We first established an animal model of PD by stereotactically injecting shRNA lentiviral particles into the left striatum. Four weeks later, dopamine neuron degeneration and Parkinsonism were confirmed by behavioral testing, western blotting, and immunohistochemistry. Midbrain samples were collected as we described earlier (Kambey et al., 2021b). Samples from five mice from the control group and five mice from the Nurr1 LV-shRNA group were enzymatically digested into single-cell suspensions before being sequenced individually on the 10× Genomics platform. Only genes that had at least one UMI count detected in at least one cell were included in the subsequent analysis. Cells that had fewer than 400 genes or more than 10% mitochondrial unique molecular identifier counts were excluded. After filtering the cells using the lowest and highest thresholds for nUMI, nGene, and mitochondrial RNA genes, 16,093 and 15,260 cells were obtained for the control and Nurr1 LV-shRNA groups, respectively. Similar results were obtained by comparing nUMI, nGene, and mitochondrial mRNA percentages (pMito) (Additional Figure 3 ^{(97.8KB, pdf)} ). Batch effects among the samples were noticed and corrected. To normalize expression levels, we used the “LogNormalize” tool of the “Normalization” function, and we decreased variables by principal component analysis to reduce the dimensions of the analysis. Figure 6A shows the total number of cells in each cluster. Figure 6B depicts the distribution of cells on UMAP in each group. We used the Seurat feature of integrating data analyses from multiple conditions to categorize the cell types in all of the samples; that is, by using UMAP’s dimensionality reduction algorithm in conjunction with unsupervised cell clustering (Figure 6C). On the basis of the independently expressed unique transcriptional patterns, 22 cell clusters were identified using the Seurat clustering tool (Figure 6C). Differential gene expression was assessed for each cluster using the Wilcox algorithm to identify marker genes (Figure 6D). The 22 clusters were annotated into six clusters-dopaminergic neurons, GABAergic neurons, glutamatergic neurons, microglia, oligodendrocytes, and astrocytes-using SingleR supplemented with manual annotation. SingleR is a cutting-edge computational technique for impartial cell type detection in scRNA-seq that uses baseline transcriptomic datasets of pure cell types to separately identify the cell of origin of each individual cell (Aran et al., 2019; Figure 6E and F).

Figure 6.

Cellular heterogeneity in the substantia nigra following silencing of the transcription factor Nurr1.

(A) The LogNormalize tool of the “Normalization” function was used to standardize gene expression values. The lower graph shows the total number of cells in each cluster. (B) UMAP plot showing clusters in the Nurr1 LV_shRNA and Nurr1 LV_scramble groups (the numbers 0–22 represent individual clusters). (C) The two groups were integrated for further analysis. (D) The “findallmarkers” tool in Seurat was used to identify the marker genes in each cluster. The numbers indicate the total number of marker genes identified in each cluster. (E) SingleR was used to automatically identify cell types, and manual annotation was used to augment the results. The results correspond to the UMAP diagram. (F) Distribution of the annotated clusters. Nurr1: Nuclear receptor-related factor 1; Nurr1 LV_Scramble: Nurr1 Lentiviral_Scramble; Nurr1 LV_shRNA: Nurr1 Lentiviral_short hairpin RNA; UMAP: Uniform Manifold Approximation and Projection.

Single-nuclei RNA sequencing identified genes that are activated in the context of Nurr1 knockdown, the majority of which encode components of major histocompatibility II complex, suggesting a mechanistic basis for the role of Nurr1 deficiency in PD

Next, we sought to identify markers that were highly expressed across all clusters. Genes enriched in each cluster were identified using the Seurat package “FindAllMarkers” (Additional Table 2 ^{(4MB, pdf)} ). Our analysis identified a number of genes previously not known to be associated with PD, the majority of which encode components of major histocompatibility complex II and were expressed at high levels in Nurr1 LV-shRNA samples. Cd74, H2-Ab1, H2-Aa, H2-Eb1, Lyz2, Mrc1, Slc47a1, Ms4a4b, and Ptprc2 were found in the oligodendrocyte cluster, and Slc6a3 was found in the dopaminergic neuron cluster. Table 1 shows the top 20 differentially expressed marker genes. A violin plot depicts the expression of these marker genes throughout the 22 cell clusters (Figure 7A and B). Figure 7C depicts the expression of these marker genes in several cell clusters using a UMAP approach. Among the top 10 genes, four interacting nodes were detected utilizing the “STRING” tool (Figure 7D). The majority of these genes are associated with the major histocompatibility complex II and encode proteins involved in antigen processing and presentation. According to SingleR analysis, which was augmented with manual cell annotation in Seurat, all of the top 10 markers identified for all of the clusters were overexpressed in the oligodendrocyte cluster (Table 1). For each of the five most significantly differentially expressed marker genes, we created a heatmap showing the levels of gene expression (Figure 7E). When pathway annotations were examined, the most significantly enriched pathway among all expressed marker genes was antigen processing and presentation (Figure 7F and Additional Table 3). Other pathways included cell adhesion molecules, allograft rejection, graft-versus-host disease, and many others. On the Gene Ontology Directed Acyclic Graph, most of the nodes are related to antigen processing and presentation (Figure 7G). GO biological process annotation of all marker genes found that the majority of the genes are involved in peptide antigen processing and presentation, antigen processing and presentation, and immune system processes, among other aspects (Additional Table 4 ^{(1.9MB, pdf)} ).

Table 1.

The top 20 differentially expressed marker genes

Gene	p_val	avg_log2FC	pct.1	pct.2	p_val_adj	Cluster
Cd74	0	5.279959	0.552	0.029	0	Oligodendrocytes
H2-Ab1	0	4.792272	0.471	0.013	0	Oligodendrocytes
H2-Aa	0	4.378477	0.471	0.016	0	Oligodendrocytes
Ms4a4b	0	4.358793	0.804	0.005	0	Oligodendrocytes
H2-Eb1	0	4.281058	0.448	0.013	0	Oligodendrocytes
Slc47a1	0	4.211992	0.717	0.004	0	Oligodendrocytes
Ptprc2	0	3.967357	0.75	0.021	0	Oligodendrocytes
Lyz2	0	3.83447	0.557	0.021	0	Oligodendrocytes
Slc6a3	0	3.822566	0.686	0.014	0	Dopamine neurons
Mrc1	0	3.809226	0.557	0.005	0	Oligodendrocytes
H2-K12	1.78E-187	3.80541	0.783	0.064	3.82E-183	Microglia
Flt1	0	3.802803	0.687	0.026	0	Oligodendrocytes
Slc18a2	0	3.694368	0.843	0.051	0	GABAergic neurons
Slc38a23	1.53E-86	3.620089	0.861	0.407	3.27E-82	Oligodendrocytes
Ptgds3	6.33E-139	3.547415	0.969	0.823	1.36E-134	Astrocytes
Slc7a111	0	3.523647	0.597	0.04	0	Astrocytes
H2-Q7	0	3.517585	0.652	0.009	0	Oligodendrocytes
Slco1a4	0	3.40731	0.582	0.018	0	Oligodendrocytes
F630028O10Rik	0	3.342438	0.505	0.002	0	Oligodendrocytes
Dcn	0	3.310526	0.468	0.026	0	Oligodendrocytes

pct.1 means the percentage of cells expressing the gene in group 1 (Nurr1 LV_shRNA). Pct.2 is the percentage of cells expressing the gene in group 2 (Nurr1 LV_Scramble).

Figure 7.

Single-nuclei RNA sequencing revealed activation of genes encoding components of major histocompatibility II complex, suggesting a possible mechanism for Nurr1 deficiency-related defects in Parkinson’s disease.

(A) Violin plot showing expression of the top 10 marker genes in the 22 cell clusters. (B) Top 10 marker genes shown by the cluster in which they were expressed. (C) UMAP plots displaying expression of the top 10 marker genes in each separate cell cluster, with each gene’s expression level represented by a different color. (D) Interaction of the top 10 marker genes as deduced using the STRING tool. (E) Gene expression heatmap for the five most significant marker genes identified from the 22 clusters. (F) KEGG pathway annotation of the 20 most significant marker genes. The horizontal coordinate is Rich factor, the size of the dot represents the number of different genes that are enriched in the term, and the color represents the P-value of the path. (G) A Gene Ontology Directed Acyclic Graph (DAG) constructed in R was used to infer/visualize the hierarchical structure of the gene ontology. Most of the nodes are related to antigen processing and presentation. The figure shows the 10 most significant GO-BP terms. UMAP: Uniform Manifold Approximation and Projection; KEGG: Kyoto Encyclopedia of Genes and Genomes; GO-BP: Gene Ontology-Biological Processes.

Additional Table 3.

Pathway’s annotation

Category	PathwayID	Pathway	List	Total	Pvalue	adjustPvalue	Gene
Organismal Systems	mmu04612	Antigen processing and presentation	9	90	3.08E-11	3.75E-09	16149/14961/14960/14969/14972/15018/14964/13040/12526
Environmental InformationProcessing	mmu04514	Cell adhesion molecules	10	174	5.39E-10	3.29E-08	14961/14960/14969/19264/14972/15018/14964/12741/80883/12526
Human Diseases	mmu05330	Allograft rejection	6	63	1.12E-07	3.42E-06	14961/14960/14969/14972/15018/14964
Human Diseases	mmu05332	Graft-versus-host disease	6	63	1.12E-07	3.42E-06	14961/14960/14969/14972/15018/14964
Human Diseases	mmu04940	Type I diabetes mellitus	6	70	2.13E-07	5.19E-06	14961/14960/14969/14972/15018/14964
Cellular Processes	mmu04145	Phagosome	8	182	2.97E-07	6.05E-06	14961/14960/14969/17533/14972/15018/14964/13040
Human Diseases	mmu05320	Autoimmune thyroid disease	6	79	4.40E-07	7.66E-06	14961/14960/14969/14972/15018/14964
Human Diseases	mmu05323	Rheumatoid arthritis	6	87	7.82E-07	1.13E-05	14961/14960/14969/14254/20315/20304
Human Diseases	mmu05416	Viral myocarditis	6	88	8.37E-07	1.13E-05	14961/14960/14969/14972/15018/14964
Organismal Systems	mmu04672	Intestinal immune network for IgA production	4	43	1.96E-05	0.000234487	14961/14960/14969/20315
Human Diseases	mmu05169	Epstein-Barr virus infection	7	231	2.11E-05	0.000234487	14961/14960/14969/14972/15018/14964/12502
Organismal Systems	mmu04640	Hematopoietic cell lineage	5	94	2.55E-05	0.000259539	14961/14960/14969/12502/12526
Human Diseases	mmu05166	Human T-cell leukemia virus 1 infection	7	247	3.26E-05	0.000305554	14961/14960/14969/14972/15018/14964/12502
Human Diseases	mmu05152	Tuberculosis	6	180	5.23E-05	0.00045538	16149/14961/14960/14969/17533/13040
Human Diseases	mmu05150	Staphylococcus aureus infection	5	124	9.65E-05	0.000784943	14961/14960/14969/12260/12259
Human Diseases	mmu05310	Asthma	3	25	0.00010994	0.000838268	14961/14960/14969
Human Diseases	mmu05322	Systemic lupus erythematosus	5	148	0.00022194	0.001592726	14961/14960/14969/12260/12259
Human Diseases	mmu05168	Herpes simplex virus 1 infection	8	458	0.00025292	0.001714244	16149/14961/14960/14969/14972/15018/14964/20304
Organismal Systems	mmu04658	Th1 and Th2 cell differentiation	4	88	0.00032733	0.002101812	14961/14960/14969/12502
Human Diseases	mmu05163	Human cytomegalovirus infection	6	256	0.00036073	0.002200437	14972/15018/14964/18595/20315/20304
Human Diseases	mmu05142	Chagas disease	4	103	0.00059608	0.003428519	12502/12260/20304/12259
Organismal Systems	mmu04659	Th17 cell differentiation	4	104	0.00061826	0.003428519	14961/14960/14969/12502
Human Diseases	mmu05321	Inflammatory bowel disease	3	62	0.00164183	0.00870883	14961/14960/14969
Human Diseases	mmu05140	Leishmaniasis	3	70	0.0023275	0.011831473	14961/14960/14969
Human Diseases	mmu05164	Influenza A	4	173	0.00402625	0.01958829	14961/14960/14969/20304
Organismal Systems	mmu04970	Salivary secretion	3	86	0.00417455	0.01958829	17105/16531/98660
Organismal Systems	mmu04610	Complement and coagulation cascades	3	93	0.00519817	0.023488022	22370/12260/12259
Organismal Systems	mmu04660	T cell receptor signaling pathway	3	103	0.00690329	0.030078638	19264/12502/12526
Human Diseases	mmu05340	Primary immunodeficiency	2	36	0.00819423	0.033644766	19264/12526
Human Diseases	mmu05145	Toxoplasmosis	3	110	0.0082733	0.033644766	14961/14960/14969
Human Diseases	mmu05170	Human immunodeficiency virus 1 infection	4	240	0.01258496	0.049527889	14972/15018/14964/12502
Human Diseases	mmu05030	Cocaine addiction	2	48	0.01425945	0.054364133	13162/214084
Cellular Processes	mmu04144	Endocytosis	4	273	0.01934939	0.071534113	14972/15018/14964/18595
Human Diseases	mmu05031	Amphetamine addiction	2	69	0.02821946	0.10125806	13162/214084
Organismal Systems	mmu04360	Axon guidance	3	181	0.0310338	0.108174956	20315/19649/80883
Cellular Processes	mmu04218	Cellular senescence	3	184	0.0323616	0.109669863	14972/15018/14964
Organismal Systems	mmu04721	Synaptic vesicle cycle	2	77	0.0345386	0.110887076	13162/214084
Human Diseases	mmu05133	Pertussis	2	77	0.0345386	0.110887076	12260/12259
Cellular Processes	mmu04510	Focal adhesion	3	201	0.04044437	0.126518269	14254/18595/22370
Organismal Systems	mmu04911	Insulin secretion	2	86	0.04223985	0.128831531	16531/98660
Human Diseases	mmu05165	Human papillomavirus infection	4	362	0.04744758	0.141185479	14972/15018/14964/22370
Environmental InformationProcessing	mmu04061	Viral protein interaction with cytokine and cytokine receptor	2	95	0.05052205	0.146754511	20315/20304
Human Diseases	mmu05167	Kaposi sarcoma-associated herpesvirus infection	3	225	0.05344206	0.147950092	14972/15018/14964
Organismal Systems	mmu04976	Bile secretion	2	100	0.05535742	0.147950092	28250/98660
Metabolism	mmu00604	Glycosphingolipid biosynthesis - ganglio series	1	15	0.05572295	0.147950092	15212
Human Diseases	mmu05203	Viral carcinogenesis	3	229	0.05578446	0.147950092	14972/15018/14964
Metabolism	mmu00603	Glycosphingolipid biosynthesis - globo and isoglobo series	1	16	0.05932868	0.154002092	15212
Organismal Systems	mmu04974	Protein digestion and absorption	2	108	0.06341896	0.161189846	67760/98660
Metabolism	mmu00511	Other glycan degradation	1	18	0.06650009	0.165571647	15212
Organismal Systems	mmu04972	Pancreatic secretion	2	114	0.06971217	0.170097692	16531/98660
Organismal Systems	mmu04670	Leukocyte transendothelial migration	2	118	0.07401858	0.177064045	20315/12741
Metabolism	mmu00531	Glycosaminoglycan degradation	1	21	0.07715781	0.177945404	15212
Organismal Systems	mmu04650	Natural killer cell mediated cytotoxicity	2	121	0.07730415	0.177945404	14972/14964
Organismal Systems	mmu04964	Proximal tubule bicarbonate reclamation	1	22	0.08068406	0.179954098	98660
Human Diseases	mmu05020	Prion disease	3	268	0.08112685	0.179954098	12260/20304/12259
Cellular Processes	mmu04142	Lysosome	2	131	0.08858196	0.192982118	15212/13040
Organismal Systems	mmu04728	Dopaminergic synapse	2	135	0.09322538	0.199535019	13162/214084
Organismal Systems	mmu04960	Aldosterone-regulated sodium reabsorption	1	38	0.135357	0.283930181	98660
Environmental InformationProcessing	mmu04022	cGMP-PKG signaling pathway	2	173	0.14045784	0.283930181	16531/98660
Metabolism	mmu00513	Various types of N-glycan biosynthesis	1	40	0.14196509	0.283930181	15212
Cellular Processes	mmu04216	Ferroptosis	1	40	0.14196509	0.283930181	26570
Environmental InformationProcessing	mmu04151	PI3K-Akt signaling pathway	3	359	0.15547602	0.305936688	14254/18595/22370
Organismal Systems	mmu04062	Chemokine signaling pathway	2	192	0.1657251	0.320079762	20315/20304
Organismal Systems	mmu04973	Carbohydrate digestion and absorption	1	48	0.1679107	0.320079762	98660
Metabolism	mmu00520	Amino sugar and nucleotide sugar metabolism	1	50	0.17427715	0.327104803	15212
Human Diseases	mmu05034	Alcoholism	2	205	0.18346392	0.334068629	13162/214084
Human Diseases	mmu05205	Proteoglycans in cancer	2	205	0.18346392	0.334068629	13179/22370
Organismal Systems	mmu04978	Mineral absorption	1	54	0.18686853	0.335264132	98660
Environmental InformationProcessing	mmu04015	Rap1 signaling pathway	2	214	0.19591401	0.346398681	14254/18595
Cellular Processes	mmu04810	Regulation of actin cytoskeleton	2	220	0.20427806	0.356027482	18595/20315
Organismal Systems	mmu04961	Endocrine and other factor-regulated calcium reabsorption	1	61	0.20845644	0.358192758	98660
Organismal Systems	mmu04623	Cytosolic DNA-sensing pathway	1	63	0.21452155	0.363494841	20304
Environmental InformationProcessing	mmu04014	Ras signaling pathway	2	232	0.2211316	0.369562391	14254/18595
Environmental InformationProcessing	mmu04020	Calcium signaling pathway	2	240	0.23244122	0.376205508	14254/18595
Human Diseases	mmu05230	Central carbon metabolism in cancer	1	69	0.23244746	0.376205508	18595
Human Diseases	mmu05218	Melanoma	1	72	0.24126088	0.376205508	18595
Human Diseases	mmu05012	Parkinson disease	2	247	0.24237343	0.376205508	13162/214084
Human Diseases	mmu05171	Coronavirus disease - COVID-19	2	247	0.24237343	0.376205508	12260/12259
Organismal Systems	mmu04918	Thyroid hormone synthesis	1	74	0.24708184	0.376205508	98660
Human Diseases	mmu05214	Glioma	1	74	0.24708184	0.376205508	18595
Organismal Systems	mmu04971	Gastric acid secretion	1	75	0.24997604	0.376205508	98660
Organismal Systems	mmu04924	Renin secretion	1	76	0.25285944	0.376205508	16531
Human Diseases	mmu01521	EGFR tyrosine kinase inhibitor resistance	1	79	0.26144524	0.384293008	18595
Metabolism	mmu00590	Arachidonic acid metabolism	1	86	0.28110804	0.3966762	19215
Cellular Processes	mmu04540	Gap junction	1	86	0.28110804	0.3966762	18595
Organismal Systems	mmu04260	Cardiac muscle contraction	1	87	0.2838752	0.3966762	98660
Environmental InformationProcessing	mmu04512	ECM-receptor interaction	1	88	0.28663202	0.3966762	22370
Human Diseases	mmu05235	PD-L1 expression and PD-1 checkpoint pathway in cancer	1	88	0.28663202	0.3966762	12502
Organismal Systems	mmu04727	GABAergic synapse	1	89	0.28937854	0.3966762	67760
Organismal Systems	mmu04666	Fc gamma R-mediated phagocytosis	1	92	0.29755666	0.403354582	19264
Environmental InformationProcessing	mmu04350	TGF-beta signaling pathway	1	95	0.3056434	0.405295332	13179
Environmental InformationProcessing	mmu04060	Cytokine-cytokine receptor interaction	2	292	0.30649575	0.405295332	20315/20304
Environmental InformationProcessing	mmu04010	MAPK signaling pathway	2	294	0.30933918	0.405295332	14254/18595
Human Diseases	mmu05231	Choline metabolism in cancer	1	98	0.31363975	0.405295332	18595
Human Diseases	mmu05215	Prostate cancer	1	99	0.31628528	0.405295332	18595
Organismal Systems	mmu04620	Toll-like receptor signaling pathway	1	100	0.31892092	0.405295332	20304
Organismal Systems	mmu04925	Aldosterone synthesis and secretion	1	102	0.32416264	0.407709707	98660
Environmental InformationProcessing	mmu04064	NF-kappa B signaling pathway	1	105	0.33195187	0.413246206	20315
Environmental InformationProcessing	mmu04668	TNF signaling pathway	1	113	0.3522996	0.42857075	20304
Organismal Systems	mmu04724	Glutamatergic synapse	1	113	0.3522996	0.42857075	67760
Environmental InformationProcessing	mmu04066	HIF-1 signaling pathway	1	114	0.35480038	0.42857075	14254
Organismal Systems	mmu04919	Thyroid hormone signaling pathway	1	120	0.36960938	0.442081809	98660
Organismal Systems	mmu04726	Serotonergic synapse	1	131	0.39590696	0.46893834	214084
Human Diseases	mmu05135	Yersinia infection	1	134	0.40289221	0.472623551	12526
Cellular Processes	mmu04210	Apoptosis	1	136	0.40750542	0.473482487	13040
Organismal Systems	mmu04270	Vascular smooth muscle contraction	1	143	0.42338082	0.487287362	16531
Human Diseases	mmu05162	Measles	1	146	0.43005747	0.490345899	12502
Environmental InformationProcessing	mmu04072	Phospholipase D signaling pathway	1	149	0.43665905	0.493263005	18595
Organismal Systems	mmu04261	Adrenergic signaling in cardiomyocytes	1	152	0.4431864	0.49604349	98660
Human Diseases	mmu05160	Hepatitis C	1	165	0.47063323	0.51934756	12741
Cellular Processes	mmu04530	Tight junction	1	167	0.47473753	0.51934756	12741
Environmental InformationProcessing	mmu04630	JAK-STAT signaling pathway	1	168	0.47677809	0.51934756	18595
Organismal Systems	mmu04621	NOD-like receptor signaling pathway	1	211	0.55760202	0.600636142	20304
Environmental InformationProcessing	mmu04024	cAMP signaling pathway	1	215	0.56447213	0.600636142	98660
Human Diseases	mmu05417	Lipid and atherosclerosis	1	216	0.56617341	0.600636142	20304
Human Diseases	mmu05202	Transcriptional misregulation in cancer	1	223	0.57790308	0.607794613	14254
Human Diseases	mmu05200	Pathways in cancer	2	543	0.62189255	0.645958812	18595/20315
Human Diseases	mmu05132	Salmonella infection	1	253	0.62477984	0.645958812	19264
Human Diseases	mmu05206	MicroRNAs in cancer	1	303	0.69190774	0.709350794	18595
Environmental InformationProcessing	mmu04080	Neuroactive ligand-receptor interaction	1	358	0.75229312	0.764831336	21334
Human Diseases	mmu05022	Pathways of neurodegeneration - multiple diseases	1	472	0.84310653	0.850074348	13162
NA	mmu01100	Metabolic pathways	2	1573	0.9888085	0.988808495	19215/15212

CD74 expression is elevated in Nurr1 knockdown mice

To ascertain whether CD74 expression is altered in Nurr1 knockdown mouse cells, we performed double immunofluorescence staining. This experiment was performed as part of the second phase of the study in which Nurr1 LV_shRNA was injected into the left striatum of the mouse brain, while the right striatum was injected with Nurr1 LV_Scramble as a control (n = 10). Dual immunofluorescence analysis indicated that the elevated CD74 expression observed in the Nurr1 knockdown group relative to the control group corresponds to dopaminergic neuron degeneration, as indicated by a decrease in TH and Nurr1 immunoreactive neurons (Figure 8).

Figure 8.

Dual immunofluorescence analysis of CD74 expression in the substantia nigra of a Nurr1 knockdown mouse model (n = 5).

CD74 (red, Alexa Fluor 594) is expressed at noticeably higher levels in the Nurr1 knockdown side of the brain compared to the control side. Dual immunofluorescence analysis indicated that the elevated CD74 expression observed in the Nurr1 knockdown group relative to the control group corresponds to the degeneration of dopaminergic neurons, as indicated by decreased numbers of TH (green, Alexa Fluor 488) and NURR1 (green, Alexa Fluor 488) immunoreactive neurons. Scale bars: 100 μm. CD74: Cluster of differentiation 74; DAPI: 4′,6-diamidino-2-phenylindole; Nurr1: nuclear receptor-related factor 1; Nurr1 LV_Scramble: Nurr1 Lentiviral_Scramble; Nurr1 LV_shRNA: Nurr1 Lentiviral_short hairpin RNA; TH: tyrosine hydroxylase.

Cd74 expression is increased in a 6-OHDA-induced mouse model of PD

To confirm the above results in a PD mouse model, mouse brains were injected 6-OHDA for 4 weeks, after which the midbrain was removed and processed. Cd74 expression in the midbrain tissues was then assessed by qPCR. In comparison to the control side, which received injections of normal saline, there was a 1-fold increase in Cd74 mRNA expression in the tissues exposed to 6-OHDA (P = 0.0141, t = 2.871, df = 12; Figure 9).

Figure 9.

Elevated Cd74 mRNA expression in the substantia nigra of a 6-OHDA-induced model of Parkinson’s disease model as compared to the control side.

To generate the model, 6-OHDA and saline solution were injected into the left and right striatum, respectively. The data are presented as the mean ± SD (n = 7). *P < 0.05 (Student’s t-test). 6-OHDA: 6-Hydroxydopamine; Cd74: cluster of differentiation 74; SNc: substantia nigra compacta.

Discussion

A remarkable number of patients who have been diagnosed with PD exhibit decreased expression of the transcription factor Nurr1 (Chu et al., 2006; Liu et al., 2012; Li et al., 2018; Kambey et al., 2021a). The decrease in dopaminergic neurons manifested by diminished TH and dopamine transporter expression after Nurr1 knockdown is consistent with findings from other studies, including studies in humans (Le et al., 1999, 2008; Chu et al., 2002, 2006). However, how does Nurr1 knockdown affect dopaminergic neurons as well as non-neuronal cells? The first possibility is that Nurr1 is expressed in non-neuronal cell types and that Nurr1 mRNA is activated in macrophages by inflammatory stimuli such as lipopolysaccharide (Barish et al., 2005; Pei et al., 2005). Nurr1 is expressed by a substantial number of glial cells, including Müller cells, the most common type of glial cell in the retina (Kubrusly et al., 2008). Another possibility could involve exosomes. Exosomes are extracellular vesicles produced by cells on a regular basis, with events such as cellular stress, cell signaling, and injury, influencing their release (Mathivanan et al., 2010). Meng et al. (2020) observed transfer of α-synuclein (α-syn) from neurons to astrocytes via exosomes, with consequent detrimental effects on astrocytes. They first investigated if exosomes generated from methamphetamine (METH)-treated neurons contain abnormal α-syn. Exosomes produced by METH-treated cells were found to carry pathogenic α-syn in both animal and cell line coculture models. Moreover, α-syn accumulation and inflammatory reactions were induced in primary cultured astrocytes by the presence of METH-exosomes in the medium. METH or α-syn decreased Nurr1 expression and increased inflammatory cytokine levels in primary cultured astrocytes (Chen et al., 2020b; Meng et al., 2020). This may imply that the effects of Nurr1 knockdown could be transferred from neuronal cells to glial cells via exosomes.

The discovery of genes responsible for PD fostered optimism that they could be used to develop new therapies (Sardi et al., 2018). Unfortunately, the journey from defective gene to new treatment option is often challenging because not all genes code for proteins, and the function of the encoded protein is not always fully understood. One strategy for addressing these issues is to infer the functional impact of gene variants by evaluating the proteins encoded by certain PD-associated genes. Proteomics approaches are being used to comprehend disease mechanisms and uncover biomarkers of disease (Chapman et al., 2014; Bracht et al., 2015). When it comes to studying potential disease-associated proteins, proteomics-based techniques deliver results that are objective, high-throughput, and quantitative (Cristea et al., 2004; Savage, 2015). In our study, we found elevated levels of genes encoding proteins that are associated with the PD pathway. This is in concordance with previously published studies. As an example, a substrate-specific proteomics study of PD showed that parkin, an E3 ubiquitin ligase implicated in PD, targets outer mitochondrial membrane proteins (Li and Cookson, 2019). In more recent work, IP followed by label-free quantitative MS linked leucine-rich repeat kinase 2 to the wingless/integrated (WNT) signaling pathway (Salašová et al., 2017; Pellegrini et al., 2018). Similarly, proteomics analysis has provided new insights into Alzheimer’s disease etiology, allowing the identification of candidate biomarkers for future diagnostic and therapeutic approaches (Zhang et al., 2018).

Inflammatory responses are a significant component of all central nervous system (CNS) diseases, particularly neurodegenerative disorders. Using single-nuclei RNA technology, our discovery elucidated the previously unknown mechanism by which Nurr1 knockdown causes dopaminergic neuron death in PD. We identified several upregulated (activated) genes, the majority of which encode components of major histocompatibility complex II; the top 10 upregulated genes were Cd74, H2-Ab1, H2-Aa, H2-Eb1, lyz2, Mrc1, Slc6a3, Slc47a1, Ms4a4b, and Ptprc2. Unlike nonspecific neuroinflammatory agents like lipopolysaccharide (LPS), Nurr1 deficiency is mechanistically linked to PD. A recent study that combined single-cell RNA sequencing with multicolor flow cytometry to profile microglia in the brains of mice injected with LPS found distinct profiles of activated microglia under inflammatory conditions that differ greatly from the proinflammatory profiles associated with neurodegenerative disease (Sousa et al., 2018). During PD progression, the brain’s resident macrophages become morphologically activated (Perry, 2012). Neuronal degeneration, whether induced by acute tissue injury or by a neurodegenerative condition that progresses over time, is correlated with activation of microglia, the resident macrophages in the brain. The microglia have been dubbed “pathology sensors” because they are extremely sensitive to all types of brain injury (Kreutzberg, 1996). When the brain is injured or affected by disease, microglia alter their morphology and begin to upregulate or express de novo a number of myeloid antigens (Perry, 2012). An important and intriguing aspect of our findings is that the highly activated genes were expressed in oligodendrocyte clusters. This is consistent with recent results from Errea and Rodriguez-Oroz (2021), lending validity to our findings. They published a human single-nuclei transcriptome atlas of the substantia nigra and discovered a strong link between PD susceptibility and oligodendrocyte-specific gene regulation, implying that oligodendrocytes may play a causative role in PD (Errea and Rodriguez-Oroz, 2021). The role of Cd74 in the brain ailments has recently been explored and verified using scRNA-seq techniques that compared healthy brains and brains compromised by autoimmune disorders, neurodegeneration, or ischemia (Gosselin et al., 2017; Jordão et al., 2019). Moreover, Cd74 upregulation was recently observed in macrophages in the context of disease (Jordão et al., 2019). Our results regarding Cd74 expression in the Nurr1 knockdown model and an increase in Cd74 mRNA expression in a 6-OHDA-induced PD mouse model warrant further investigation.

To justify the relevance of major histocompatibility II-related genes in PD and other neurodegenerative disorders, Harms et al. (2013) used an in vivo mouse model produced by viral overexpression of α-syn as well as in vitro systems to investigate the role of the MHCII complex in α-syn-driven neuroinflammation and neurodegeneration. In an α-syn-induced rat model of PD, T cell infiltration and MHCII overexpression in microglia lead to accelerated neuronal degeneration (Subbarayan et al., 2020). Genetic studies have linked SNPs in the HLA-DR gene to sporadic late-onset PD (Hamza et al., 2010). Myriad studies have reported a link between PD and single-nucleotide polymorphisms in the MHC class II gene (Wissemann et al., 2013; Zhu et al., 2015; Chang et al., 2020), and mutation of the MHC class II gene results in similar symptoms to Alzheimer’s disease (Mattiace et al., 1990; Perlmutter et al., 1992). These studies suggest that the MCH II pathway is critical to neuroinflammation in PD.

A limitation of this study is that we did not target specific glial cells when exploring the mechanism by which Nurr1 knockdown affects dopaminergic neurons. Nurr1 affects dopaminergic neurons through glial cells (microglia, astrocytes, oligodendrocytes) (Jakaria et al., 2019; Oh et al., 2020). The use of cre and lox mice targeting specific cells, for example Nurr1^Cd11b-cre (deletion of Nurr1 in microglia), Nurr1^Oligo2-cre (deletion of Nurr1 in oligodendrocytes), or Nurr1^Gfap-cre (deletion of Nurr1 in astrocytes), would have probably provided a different picture of the effect of Nurr1 deficiency on dopaminergic neurons. In conclusion, our findings demonstrate the mechanism for Nurr1-deficiency-based disruption of dopaminergic neurons in PD and provide a potential target for future drug development.

Additional files:

Additional file 1 ^{(735.7KB, pdf)} : Detailed in vitro, quantitative polymerase chain reaction, and immunohistochemistry experiments.

Additional file 1

Detailed in vitro, qu antitative polymerase chain reaction, and immunohistochemistry experiments

Additional file 2 ^{(93.9KB, pdf)} : Open peer review report 1.

Additional Figure 1 ^{(77.9KB, pdf)} : The mechanism on how the shRNA plasmid gene silencers were produced.

Additional Figure 1

The mechanism on how the shRNA plasmid gene silencers were produced.

shRNA: short hairpin RNA; siRNA: silencer RNA.

Additional Figure 2 ^{(118.1KB, pdf)} : The gene ontology of mass spectrometry (LC-MS/MS) results.

Additional Figure 2

The gene ontology of mass spectrometry (LC-MS/MS) results.

LC-MS/MS: Liquid chromatography coupled with tandem mass spectrometry.

Additional Figure 3 ^{(97.8KB, pdf)} : Distribution of basic cell information in each sample before and after cell filtration.

Additional Figure 3

Distribution of basic cell information in each sample before and after cell filtration.

nCount_RNA: Total number of molecules detected; nFeature_RNA: comparable number of unique molecular identifiers; Nurr1: nuclear receptor-related factor 1; Nurr1 LV_Scramble: Nurr1 Lentiviral_Scramble; Nurr1 LV_shRNA: Nurr1 Lentiviral_short hairpin RNA; Percent.mt: mitochondrial mRNA percentages.

Additional Table 1 ^{(376.4KB, pdf)} : Differentially expressed proteins on the label-free liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS).

Additional Table 1

Differentially expressed proteins on the label-free liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS).

Additional Table 2 ^{(4MB, pdf)} : “FindAllMarkers” genes enrichment.

Additional Table 2

“FindAllMarkers” genes enrichment.

Additional Table 3: Pathway's annotation.

Additional Table 4 ^{(1.9MB, pdf)} : The Gene Ontology biological process annotation function of all marker genes.

Additional Table 4

The Gene Ontology biological process annotation function of all marker genes.