Interferon regulates neural stem cell function at all ages by orchestrating mTOR and cell cycle (2022)
Figure 4

Figure 4. Interferon regulates stemness and fine-tunes progenitor production at all ages

A. Graphical depiction of the mathematical model describing the activation (at rate r) of quiescent neural stem cells (qNSC) into active NSC (aNSC) followed by their progression through cell cycle (at rate pₛ) into either two downstream progenitors (at probability 1−b) or two NSCs (at probability b) that return to quiescence. In models with time-dependent parameters, b ≡ b(t) and r ≡ r(t), See Appendix supplementary methods for details.

B. vSVZ niche FACS quantifications of total NSCs (Total NSCs) & immunofluorescence quantification of aNSCs (Fraction of aNSCs) in WT or IFNAGR KO mice at 2, 7 and 22 months old (MO). Data from individual mice is plotted. Inferred dynamics are depicted as a blue (WT) or red (IFNAGR KO) line over time. Sample sizes: WT total at 60 days: 8 mice, WT total at 210 days: 3 mice, WT total at 660 days: 3 mice, WT fraction aNSCs at 60 days: 3 mice, WT fraction aNSCs at 660 days: 4 mice, IFNAGRKO total at 60 days: 8 mice, IFNAGRKO total at 180 days: 3 mice, IFNAGRKO total at 660 days: 3 mice, IFNAGRKO fraction aNSCs at 60 days: 3 mice, IFNAGRKO fraction aNSCs at 660 days: 3 mice.

C. Inferred parameters are depicted as a blue (WT) or red (IFNAGR KO) line over time.

D. Induced interferon knockout simulations (colored, each color represents a different intervention timepoint) against wildtype simulations (black) showing the total number of stem cells across age. Dashed red line denotes age 350 days.

E. The rate at which progenitors are produced (2 ps (1 − b) aNSC) from NSCs for induced interferon knockout simulations with interferon dependent self-renewal (coloured, each colour represents a different intervention timepoint) and WT simulations (black). Dashed red line denotes age 350 days.

Materials and Methods

Mice

C57BL/6 male mice were purchased from Janvier or bred in-house at the DKFZ Center for Preclinical Research. Male IFNAGRKO (Huang et al, 1993; Müller et al, 1994) (IFNAR-/-IFNGR-/-) were backcrossed to a C57BL6 background. Male TiCY mice (Tlx-CreERT2-YFP) (Liu et al, 2008) (TiCY [B6-Tg(Nr2e1-Cre/ERT2)1GscGt(ROSA)26Sortm1(EYFP)CosFastm1Cgn/Amv]) were crossed with IFNAGRKO mice to generate TiCY-IFNAGRKO mice ([B6-Tg(Nr2e1-Cre/ERT2)1Gsc Gt(ROSA)26Sortm1(EYFP)CosFastm1CgnIfnar1tm1AgtIfngr1tmAgt/Amv]). Animals were housed under standard conditions and fed ad libitum. All procedures were approved by the Regierungpräsidium Karlsruhe.

Cell culture and treatments

For NSC isolation, the subventricular zones from 8-12 weeks old male C57BL/6 mice were isolated as described (Mirzadeh et al, 2010). The tissue was dissociated using the Neural Tissue Dissociation Kit with Papain (Miltenyi Biotec) following the manufacturer's instructions. Cells were cultured in Neurobasal A Medium supplemented with 2% B27 Supplement serum-free, 1% L-Glutamine (all from ThermoFisher), 2 µg/ml of heparin, 20 ng/ml of human basic FGF (ReliaTech) and 20 ng/ml of human EGF (Promokine). Cells were maintained in a 37°C, 5% CO2 incubator.

For the treatment with interferon, mouse recombinant IFN-β (Millipore) diluted in DPBS with 0.1% Bovine serum albumin BSA (Roche) were added directly to the culture media at final concentration of 40 u/ml for the indicated time. Control cells were treated with the same volume of 0.1% BSA in PBS.

For the treatment with Torin1 (Cay10997), Torin1 was dissolved in 100% DMSO and added to cells at 250 nM for the indicated times. Control cells were treated with the same volume of DMSO. For the treatment with Rapamycin (Cay13346), Rapamycin was dissolved in 100% DMSO and added to cells at 100 nM for the indicated times. Control cells were treated with the same volume of DMSO. For the treatment with ISRIB (Sigma), ISRIB (Sidrauski et al, 2015) was dissolved in 100% DMSO and added to cells at 500 nM for the indicated times. Control cells were treated with the same volume of DMSO.

CRISPR-mediated generation of TSC2+/- NSCs

CRISPR-Cas9 gRNAs targeting exon 2 of Tsc2 (ENSMUSG00000002496) were designed using the CCTop target online predictor (Stemmer et al, 2015) and cutting efficiency was evaluated by T7EI Assay using T7 Endonuclease I (NEB). We selected the TSC2Ex2-gRNA (TGTTGGGATTGGGAACATCGAGG) and cloned it into the Cas9-containing plasmid pSpCas9(BB)-2A-GFP (Addgene) as described (Ran et al, 2013) (see Table EV2).

For nucleofection, 1 x 106 NSCs were mixed with 2.5 µg of corresponding DNA using Amaxa P4 Primary Cell 4D-Nucleofector X Kit S (Lonza) with CA137 programme in a 4D-Nucleofector X Unit (Lonza). Cells were incubated for 24 hours at 37°C and 5% CO2. GFP-positive single NSCs were FACS-sorted into 96-well plates. Single NSC colonies were expanded and TSC2 abundancy was checked by western blot (see section below).

Flow cytometry analysis

For the flow cytometry analysis, vSVZ single cell suspensions from mice at the specified ages were generated and stained for flow cytometry analysis as described (Llorens-Bobadilla et al, 2015). Briefly, vSVZ was microdissected using the Neural Tissue Dissociation kit with Trypsin and Gentle MACS dissociator (Miltenyi). Cells were stained with the following antibodies: O4 APC-Cy7 (1:100), GLAST (ACSA-1)-PE (1:50) (all from Miltenyi), Ter119 APC-Cy7 (Biolegend; 1:100), CD45 APC-Cy7 (BD; 1:200), Prominin1-APC (eBioscience; 1:75), Alexa488::EGF (Invitrogen; 1:100), and Sytox Blue (Invitrogen, 1:500). For analysis, we size selected the vSVZ cells and excluded for doublets, dead cells and CD45+/Ter119+/O4+ cells as recently described (Kalamakis et al, 2019). Signal acquirement was performed on a BD FACSCanto II at the DKFZ Flow Cytometry Facility and results were analysed using FlowJo v.10.

Cell cycle analysis - CycleFlow

Cell cycle was assessed using CycleFlow as described (Jolly et al, 2022) with some modifications. CycleFlow was applied to NSCs with or without a pre-treatment of IFN-β. 24 h after cell seeding, either vehicle or IFN-β was added to NSCs for 17 h (this step was skipped in the non-pre-treated NSCs). Then, EdU from the Click-iT Plus EdU Flow Cytometry Assay Kit (ThermoFisher) was added at 10 µM. Cells were incubated 1 h at 37°C 25% CO2. Cells were collected, washed with DPBS and resuspended in pre-warmed medium containing either vehicle or IFN-β. Cells were then divided in different wells to be incubated and collected at the indicated incubation times (see Figure 1 and S1). At the collection time, cells were collected, dissociated (Accutase®, Sigma) and stained for viability with Zombie Red (Bioleged) following the manufacturer's recommendations. After that, cells were washed and PFA-fixed using the Click-iT Plus EdU Flow Cytometry Assay Kit (ThermoFisher). Fixed cells were kept in 90% methanol DPBS at -20°C until completion of the time-course. Upon all collections, the Click-iT reaction was performed following the manufacturer's recommendations in combination with a final staining with the DNA dye FxCycle Violet reagent (ThermoFisher; 1:1000). Signal acquirement was performed on a BD LSRFortessa Flow Cytometer and results were analysed using FlowJo v.10. Doublets and non-viable cells were excluded from the analysis. Doublets were discriminated using the DNA stain area vs. width. Cell-cycle progression mathematical inference was performed using uniform priors as already described (Jolly et al, 2022).

Global protein synthesis O-propargyl-puromycin (OPP) assay

8-well glass chambers (lab-tek) were pre-coated with poly-D Lysine (Gibco) overnight followed by additional coating with laminin (Sigma). Cells were plated and treated two days after with IFN-β or ISRIB for the indicated times. OPP (Thermo) was dissolved in DMSO and added 1 hour before collection to all conditions at a concentration of 50 µM. Cells were processed using the Click-iT Plus OPP Alexa Fluor 488 Protein Synthesis Assay Kit (Thermo) following the manufacturer's instructions excluding the DNA staining. Cells were finally mounted using fluoromount G with DAPI (ebioscience) for DNA staining. Five representative images were acquired per sample using a Leica TCS SP5 II confocal microscope with 40x magnification. OPP intensity was quantified using Fiji and normalized to the vehicle-treated samples. Control treatments consisting of no-OPP (negative control), N2 supplement (Gibco; positive control) and cycloheximide (CHX) (Sigma; negative control) were always run and processed in parallel.

Polysome profiling

NSCs were incubated with either vehicle or IFN-β as above explained. Right before cell collection, 100 µg/ml cycloheximide (CHX, EDM Millipore) in H2O was added to cells and they were incubated 5 min at 37°C 25% CO2. Cells were collected by centrifugation, washed with ice-cold DPBS containing 100 µg/ml cycloheximide (CHX), and lysed in the Polysome lysis buffer (20 mM Tris-HCl, pH 7.4, 120 mM KCl, 5 mM MgCl2, 14 mM β-mercaptoethanol, 1% NP-40, 100 µg/ml CHX, 1x complete protease inhibitor cocktail (Roche), 100 u/ml RNase Inhibitor (Thermo Fisher)). Lysates of differently treated NSC were loaded at equal amounts for total RNA onto preformed 17.5%-50% gradient of sucrose prepared in 20 mM Tris-HCl pH 7.4, 120 mM KCl, 5 mM MgCl2, 2 mM DTT, 100 µg/ml CHX. Free mRNPs, ribosome subunits, and polysome complexes were resolved by centrifugation for 2.5 h at 35,000 rpm in a SW41 rotor. The content of the tubes was fractionated with the Density Gradient Fractionation System (Teledyne ISCO) allowing for the recording of absorbance at 254 nm. 1 ml fractions were collected for RT-qPCR processing.

RT-qPCR

Total RNA was extracted from cell cultures with the Arcturus PicoPure RNA Isolation Kit (Applied Biosystems). For the fractions of polysome profiling, RNA was purified via phenol/chloroform extraction as described (Faye et al, 2014). cDNA was synthesized with the SuperScript VILO cDNA synthesis kit (Thermo Fisher). DNA amplification and detection was performed with the PowerSYBR Green PCR MasterMix (Applied Biosystems) in C1000 Touch Thermal Cycler (Bio-Rad) using QuantiTect (Qiagen) primers (see Table EV2: RT-qPCR primers). For the calculation of relative gene expression, the ΔCt method was used. The distribution of analyzed mRNAs across polysome fractions was presented as a percentage of the total amount of the mRNAs for all fractions.

Ribosomal footprinting

Cells were treated with vehicle or IFN-β 48 hours after seeding and incubated at 37°C 25% CO2 for the corresponding incubation time. Then, NSCs were incubated for 5 min with 0.1 mg/ml CHX at 37°C 25% CO2. Cells were collected, washed with DPBS supplemented with CHX, and lysed in 150 μl of the Polysome lysis buffer (see Polysome profiling section). Lysates containing 40 μg of total RNA were treated with 100 U of RNaseI (Ambion) for 45 min at room temperature, and the reaction was stopped by addition of 50 U of SUPERaseIn (Ambion). Generated 80S monosomes were collected via centrifugation through 25% sucrose cushion at 100,000 rpm for 1 h in a TLA100.2 rotor, and the pelleted RNA was extracted with the Direct-Zol RNA Miniprep kit (Zimo). Ribosomal RNA was depleted with the Gold Ribo-Zero kit (Illumina). Resulted RNA samples were resolved by electrophoresis in a 15% NOVEX TBE-Urea gel (Thermo Fisher). Gel slices including ribosome footprints corresponding to a fuzzy band of the 26-34 nt size range were cut out from the gel, crushed by centrifugation in 0.5 ml gel-breaker tubes (Segmatic) and extracted in 0.5 ml 10 mM Tris-HCl pH 7.0 by incubation for 15 min at 70°C. RNA was recovered by precipitation with isopropanol in presence of 0.3 M sodium acetate pH 5.5 and 20 μg Glycoblue (Thermo Scientific). Recovered RNA was dephosphorylated with T4 PNK (NEB) in the absence of ATP. Indexed libraries were generated using the SMARTer smRNA-Seq kit for Illumina (Takara).

For the parallel total mRNA sequencing, we used the same NSC lysates prepared as described above at the amount of 20 μg of total RNA. RNA was extracted with phenol/chloroform as described(Faye et al, 2014). RNA samples were treated with 3 U of TURBO DNase (Ambion) for 15 min at 37°C. RNA was again extracted with phenol/chloroform and recovered with ethanol precipitation in the presence of 0.3 m sodium acetate pH 5.2. Ribosomal RNA was depleted with the Gold Ribo-Zero kit (Illumina). After depletion 80 ng of each RNA sample was used to synthesized cDNA and make a library using the NEBNext Ultra Directional RNA library kit for Illumina (NEB).

Libraries quality was assessed using the Bioanalyzer 2100 (Agilent) and were sequenced in HiSeq 2000 v4.

For the analysis, Reads were trimmed applying the tool TrimGalore version 0.4.4_dev. The adaptor sequence "AGATCGGAAGAGC" (Illumina TruSeq, Sanger iPCR; auto-detected) as well as 3 bp from the 5' end and 15 bp from the 3' end were removed. In addition, sequences that became shorter than 18 bp (after quality trimming) were removed using TrimGalore's default settings. After trimming reads had a length of 33 bp on average. Subsequently, reads were mapped to the mm10 transcriptome build GRC38.93 from ENSEMBL using boWTie version 0.12.7 with its standard options. Reads falling into genes were counted from BAM files applying a suited R/Bioconductor workflow (function SummarizeOverlaps with mode 'Union'). Duplicated reads were removed. For ribosomal footprinting samples, reads in the whole gene body or in the coding part of genes (CDS) were counted separately, for total RNA samples reads in the whole gene body were counted. Next, to get an estimation of the translation efficiency (TE) per gene, log-fold-changes between ribosome protected reads from the CDS and total RNA samples were computed applying DESeq2. DESeq2 was chosen as it is considered a standard tool for modelling negative-binomial distributions arising in sequencing experiments. The likelihood ratio test (LRT) was applied on the TE analysis upon IFN-β treatment. Genes with positive fold-changes are considered to be enhanced, those with negative ones to be repressed. The gene set enrichment analysis was performed using the clusterProfiler tool from Bioconductor using fold-changes for all expressed genes. Gene ontology terms related to "biological process" with an FDR < 0.05 and highest significance were selected for the plots. TOP-mRNAs (Dataset EV3) were defined as mRNAs with a cytidine immediately after the 5′ cap, followed by an uninterrupted stretch of 4-14 pyrimidines (Thoreen et al, 2012).

Proteome and phosphoproteome

Cells were treated with vehicle or IFN-β and incubated at 37°C 25% CO2 for the time specified in the figures. Cell lysates for the proteome and phosphoproteome analysis were prepared as described (Potel et al, 2018) with some modifications. Briefly, cells were collected, washed and weighted to determine the mass of cells pellets. One volume of the cell pellet was resuspended in six volumes of proteome lysis buffer composed of 100 mM Tris-HCl pH 8.5, 7 M Urea, 1 mM MgCl2, 1 mM sodium ortovanadate, 1% Trition X-100, 1X PhosphoSTOP inhibitor (Roche), 1X Complete EDTA free protease inhibitor (Roche). To improve lysis, cells suspension was sonicated 3 times for 10 s (1 sec on, 1 sec off) with 30 s pause at 40% output using the Fisherbrand Model 120 Sonic Dismembrator (Fisher Scientific). The lysate was clarified by centrifugation at 21000 g, for 1 h at 4°C. Lysates were incubated for 2 h at room temperature. Protein concentration was determined with the Pierce BCA Protein Assay Kit (Thermo Fisher) and 400 µg of total protein for each sample were subjected to metal-affinity enrichment & mass spectrometry at the DKFZ Genomics and Proteomics Facility. Proteins and phosphopeptides were quantified and analyzed for differential abundance using Perseus45.

Western blot analysis

Cells were collected, washed with DPBS and resuspended in 100 µl of 0.2% SDS, 1 mM vanadate, 1X cOmplete Protease inhibitor cocktail (Roche), 1X PhosphoSTOP inhibitor (Roche) and pass several times through a 23G syringe needle. 15 µg of total protein from cellular lysates were resolved in NuPAGE 10% gels (Thermo Fisher) and transferred to nitrocellulose membranes. The membranes were blocked in 5% BSA in TBS-T buffer and probed overnight with primary antibody. Primary antibodies used: Akt (#9272), phospho-Akt S473 (#9271), 4E-BP1 (#9644; clone 53H11), phospho-4E-BP1 S65 (#9451), eiF2alpha (#5324S; clone D7D3), phospho-eIF2α S51 (#3597; clone 119A11), Rb (#9313; clone D20), phospho-Rb S780 (#8180; clone D59B7), p70S6K (#2708; clone 49D7), phospho-p70S6K T389 (#9234; clone 108D2), phospho-Rps6 S235/236 (#4857; clone 91B2), phospho-Rps6 S240/244 (#2215), TSC2 (#4308; clone D93F12), phospho-TSC2 T1462 (#3617; clone 5B12) and LARP1 (#14763) are all rabbit antibodies from Cell Signalling. Rps6 (#ab40820) and Vinculin (#ab129002; clone EPR8185) are rabbit antibodies from Abcam. Rabbit anti-actin (#sc-1616-R; clone I-19) from Santa Cruz. Mouse anti-phospho-Akt T308 (#A1124-07R) from USBiological. Rabbit Ifitm3 (11714-1-AP) from ptglab. Rabbit phospho-TSC2 S1452 was kindly provided by Aurelio Teleman. Membranes were washed and reprobed with secondary anti-rabbit or anti-mouse HRP-antibody (Dianova). The signals were developed with the Western Lightning Plus ECL substrate (Perkin Elmer) and scanned using the ChemiDoc Touch Imageing System (Bio-Rad). Loading controls were always run on the same blot than the quatified phospho- or total protein. The Image Lab version 5.2 (Bio-Rad) was used for band intensity quantification.

Luciferase assay

For nucleofection, 3 x 106 NSCs were mixed with 5 µg of corresponding DNA with the Amaxa P4 Primary Cell 4D-Nucleofector X Kit S (Lonza) and pulse was delivered using the CA137 programme in a 4D-Nucleofector X Unit (Lonza). Afterwards, the cells were washed, resuspended in medium and separated in three technical replicates. After recovery, cells were treated with IFN-β, Torin1, a combination of both or their corresponding vehicles as stated in the figures. Then, cells were washed and lysed in 1x Passive Lysis buffer from the Dual-Luciferase Reporter Assay System (Promega). Lysis was proceeded on an orbital shaker for 15 min at room temperature. Luciferase Assay reagent was added to each sample, mixed and all volume was transferred into a 96 well White Cliniplate (Thermo Fisher). For Firefly luciferase activity the plate was scanned in a Synergy LX mutli-mode reader (BioTek). Then 100 µl of Stop&Glo reagent was added and the plate was scanned again for Renilla luciferase. Renilla luminescence was normalized to Firefly luminescence (with technical triplicates) and the final results were presented as fold change to the control samples.

Plasmid construction

DNA constructs with different WT and mutated 5'UTRs of mouse Sox2 mRNA were assembled in the psiCheck-2 vector (Promega) including synthetic Renilla luciferase gene (hRluc) driven by SV40 early enhancer/promoter and synthetic Firefly luciferase gene (hluc+) under HSV-TK promoter. The UTRs flank hRluc open reading frame. The full-length 5'UTRs of mouse Sox2 (NM_011443.4) and Rps21 (variant2, NM_025587.2) as well as their mutated variants carrying the deletions of PRM in Sox2 (5UTRmut Sox2; CTCTT deleted), PRM in Rps21 (5UTRmut Rps21; TCCTTTC deleted) were synthesized and inserted into pEX-K4 (Sox2) or pEX-A2 (Rps21) by Eurofins. The plasmids were used to amplify the UTRs with Phusion High-Fidelity DNA polymerase (NEB) using corresponding primers carrying SfiI and NheI restriction sites to allow the inserts between SV40 promoter and the reading frame of hRluc. The used primers are listed in Table EV2. The full-length 5'UTR of mouse beta actin (Actb) mRNA (NM_007393.5) was generated by amplification of cDNA library prepared from NSCs isolated from C57BL/6 mice. For generation of the 5'UTR Actb, the used PCR primers were flanked with SfiI and NheI sites to allow the insertion in front of hRluc reading frame in psiCheck-2 vector (Promega). The accuracy of cloning was verified by sequencing of the inserts in all generated plasmids from both directions with corresponding primers for sequencing (see Table EV2).

Single-cell transcriptomics

To characterize the single-cell transcriptomics of NSCs and their progeny, we made use of TiCY (referred to as IFNAGRWT) and TiCY-IFNAGR KO mice (referred to as IFNAGRKO). In these mice, tamoxifen-induced Cre recombination takes place in neural stem cells in the vSVZ, which express Tlx (Nr2e1) (Liu et al, 2008), and will stably activate the production of eYFP labelling NSCs and their progeny.

Tamoxifen (TAM) injection was done in three days as follows: two doses daily of 1mg of tamoxifen in 100 uL of a solution of 10 mg/ml of TAM in EtOH 10% diluted on sunflower seed oil. TiCY- IFNAGRWT young mice were injected with TAM at 10 weeks old and were sacrificed 6 weeks afterwards. TiCY- IFNAGRWT old mice (71 weeks old), TiCY-IFNAGRKO young (7 weeks old) and old mice (85 and 98 weeks old) were injected with TAM and were sacrificed 9 weeks afterwards. After animal sacrifice, the vSVZ, striatum, rostral migratory stream and olfactory bulb was isolated. These latter three tissues were pooled together and referred to as Rest of the Brain (RoB). Tissues were processed as described previously (Kremer et al, 2021) and sorted in a BD FACSAria II at the DKFZ Flow Cytometry Facility. For sorting, we size-selected the vSVZ or RoB cells and excluded for doublets, dead cells and CD45+/Ter119+/O4+ cells as recently described (Kalamakis et al, 2019). We sorted vSVZ eYFP+ cells and GLAST+ cells. From the RoB, cells that were eYFP+ and eYFP-/PSANCAMlow were sorted. For every condition, 2 mice were used whose cells were labelled with Hashtags oligos (HTO) using the Cell Hashing protocol from Biolegend (TotalSeq-A). All the sorted cells were pooled on the same tube and processed in the Chromium Next GEM Chip G using Chromium Next GEM Single Cell 3' v3.1.

Gene expression libraries were prepared following the manufacturer's protocol (Chromium Next GEM Single Cell 3' v3.1) at the Single-cell open lab at DKFZ and sequenced on a NextSeq2000 V3 PE 100bp at the Sequencing open lab provided by the DKFZ Genomics and Proteomics Core Facility. Each condition (genotype and age combination) was processed on a separate 10X reaction. Hashtags libraries were processed according to the manufacturer's instruction (Biolegend) and were sequenced on a NextSeq 500 Mid output PE 75bp at the DKFZ Genomics and Proteomics Core Facility.

Single-cell transcriptomic data analysis

To derive a "NSC type I Interferon Response" signature we used the 16h IFN-β treated Ribo-Seq transcriptomic libraries. Reads were trimmed using trimgalore 0.6.6 using default settings and mapped and quantified using STAR 2.7.7a with --quantMode GeneCounts. Star outputs were concatenated and read into R 4.0.5 where DESeq2 1.30.1 was used to compute differential expression between treated and untreated samples. The genes with the 300 most significantly changed genes after multiple-testing correction from a one-sided Wald test (increased expression) were chosen as our "NSC Type I Interferon Response" gene set.

For the single-cell libraries analysis, we filtered them using the 10x index-hopping-filter 1.0.1 and thereafter quantified using kallisto|bustools's lamanno workflow (kb_python 0.26.3). Hashtag libraries were quantified using CITE-seq-Count 1.4.5. Count matrices were read into scanpy 1.6.0 and preprocessed by filtering out genes present in less than three cells and cells with less than 100 genes. Doublets were called and removed with scrublet 0.2.1. Cells with less than 10% mitochondrial UMIs, 70-6000 genes and 1000-20000 UMIs were retained as quality cells. Counts were normalised (to a target sum of 10000) and log1p transformed using scanpy methods. A UMAP was computed from 50PCs computed on scaled counts. Main clusters were identified in UMAP using DBSCAN (scikit-learn 0.22.2.post1). An auxilliary leiden clustering was computed. The largest DBSCAN cluster was identified as the lineage cluster based on marker expression. Non-subventricular astrocytes were identified and removed from this cluster by inspecting the fraction of SVZ hashtags in each cell. Other clusters were named by summed expression of celltype specific-markers (Pecam1, Prom1 and Cldn5 for endothelials, Itgam and Ptprc for microglia, Rbfox3, Calb2, Nefl and Th for neurons). Diffusion pseudotime (from scanpy) was run on the lineage cluster using the cell with the highest Aqp4 expression as the root cell. Pseudotime was then binned into qNSC1, qNSC2, aNSC, TAP and NB based on visual inspection of the expression of well-known markers Aqp4, Egfr, Dcx, Mki67, S100b. Hashtag samples were called for each cell as the maximum posterior from a multinomial mixture model (R 4.0.5 package mixtools 1.2.0) with 5 components (one for each hashtag and an extra component for no hashtags).

Gene set scores were then computed for each cell by summing up the normalised (per cell to 1000000 UMIs) and log1p-transformed expression of genes from a given set. Group-wise gene set scores were then computed as the avearge of all of the cell's scores in a given group of interest. Scores were computed for each replicate, celltype, age and genotype combination for MSigDB's "HALLMARK_INFLAMMATORY_RESPONSE", "HALLMARK_INTERFERON_ALPHA_RESPONSE" genesets, as well as the gene set identified by Wu et al. 2018 (Wu et al, 2018) and our "NSC Type I Interferon Response".

IFNAGR KO Modelling

We built on the previously established mathematical model of cell population dynamics (Kalamakis et al, 2019), describing counts of quiescent (non-cycling) and active (cycling) stem cells. The model is given by a system of ordinary differential equations:

$$\frac{d}{\text{dt}}\mathrm{\text{qNSC}} = - r \cdot \mathrm{\text{qNSC}} + 2 \cdot b \cdot p_{s} \cdot \mathrm{aNSC,}$$

$$\frac{d}{\text{dt}}\mathrm{\text{aNSC}} = r \cdot \mathrm{\text{qNSC}} - p_{s} \cdot \mathrm{\text{aNSC.}}$$

The parameter r describes the activation rate that may exponentially decrease in time

r(t) = r0 ⋅ e − βr ⋅ t.

Self-renewal is modelled by a function b

$$b\left( t \right) = \frac{1}{2}\left( 1 + e^{- \beta_{b} \cdot t} \cdot \left( 2 \cdot b_{0} - 1 \right) \right).$$

The choice of this function follows the assumptions that self-renewal probability is not larger than ½ and may be a constant or increasing in time function.

The remaining parameter ps = log (2)/(17.5/24) ≈ 0.9506 corresponds to the cell cycle rate. It was chosen from literature, following our previous publication (Kalamakis et al, 2019). In summary, the model is characterised by free parameters r0, b0, βb and βr. The initial value additionally provides the parameter NSC0 which was used to compute initial values for the specific compartments from the steady state ratio (Appendix supplementary methods).

Parameters were estimated using a multi-start box-constrained weighted-least-squares approach. The objective functions were built using new FACS quantifications for the total numbers of stem cells and the fraction of actively cycling neural stem cells from our previous publication. We applied solvers provided and chosen by the DifferentialEquations.jl package. Weights were computed as the inverse standard deviation for each age and genotype combination. Starting values were sampled from within reasonable bounds using Latin Hypercubes computed by the LatinHypercubeSampling.jl library, each was then optimized using a box constrained optimizer from the Optim.jl library. The best parameter estimates were kept as the optimum. To aid in model selection, AICc was computed for each model and dataset combination.

Finally, we estimated parameters for models with population-dependent self-renewal probabilities, described by a Hill function:

$$b\left( \mathrm{\text{NSC}} \right) = 1 - \frac{1}{1 + \left( \frac{k_{b}}{\mathrm{\text{NSC}}} \right)^{n_{b}}}.$$

We estimated these with a single kb for both genotypes. The parameter nb was estimated individually per-genotype (IFN-dependent self-renewal) or shared (IFN-independent self-renewal) to provide the two scenarios of self-renewal dependence on interferons.

IFNAGR KO simulations at arbitrary time points

To simulate the age-specific dynamics of the IFNAGR KO, we built a model with a time-dependent switch in the parameter functions. At the intervention age, the parameter functions are switched from the previously estimated WT parameter functions to their IFNAGR KO counterparts. We simulated this intervention model for varying intervention ages. For each simulation we computed the number of stem cell loss at 660 days age compared to the wildtype. We also computed the progenitor production as the cell flux from active neural stem cells to progenitors. We then integrated progenitor production from 0 to 700 as the total life-long progenitors produced.

Statistical analysis

Biological replicates "n" in the figures refer to biological replicates as of either different mice or different NSCs cultures extracted from different mice. Plotted bars represent mean and plotted error bars represent standard deviation from different biological replicates. Only biological replicates were considered for statistical analyses. Statistical tests were performed as indicated in each figure legend with a significance level of α = 0,05. To test for biphasic response in western blot data, linear models explaining log2 fold changes were fitted with a single explanatory variable of time being greater than a change time. To find this change time, models were fitted for varying change times and the model with the lowest AIC was chosen. To determine the significance of this biphasic response, one-way ANOVA was performed on this model.

Data availability

The datasets and compute code produced in this study are available in the following databases:

Single-cell RNA-seq and Ribo-Seq data have been deposited at the Gene Expression Omnibus (GEO) and are publicly available with GEO Accession Number GSE197217 as of the date of publication.

All original code is deposited at https://github.com/Martin-Villalba-lab/Data/ and publicly available as of the date of publication.

Any additional material, data or information required to reanalyse the data reported in this paper is available from the lead contact upon request. Illustrations were created with Adobe Illustrator or with BioRender.com.

Acknowledgments

We thank S. Wolf & D. Helm from the DKFZ Genomics and Proteomics Core Facility; S. Schmitt from the DKFZ Flow Cytometry Core Facility; J.-P. Mallm from the DKFZ scOpenLab; the DKFZ Centre for Preclinical Research; K. Volk, K. Menzner & H. Abendroth for technical assistance; B. Bukau and the members of the Martin-Villalba laboratory for critical comments.

This work was supported by the European Research Council (ERC; REBUILD_CNS), the Helmholtz Zukunftsthema ageing and Metabolic Programming (AMPro) ZT-0026, the German Research Foundation (DFG; SFB873), and the German Cancer Research Center (DKFZ). S. A. is supported by the Klaus-Tschira-Stiftung.

Disclosure statement and competing interests

The authors declare no competing interests. Ana Martin-Villalba is an EMBO member. This has no bearing on the editorial consideration of this article for publication.

References

Alspach E, Lussier DM & Schreiber RD (2019) Interferon γ and its important roles in promoting and inhibiting spontaneous and therapeutic cancer immunity. Cold Spring Harb Perspect Biol 11: 1-22Artegiani B, Lindemann D & Calegari F (2011) Overexpression of cdk4 and cyclinD1 triggers greater expansion of neural stem cells in the adult mouse brain. J Exp Med 208: 937-948Avni D, Biberman Y & Meyuhas O (1996) The 5′ Terminal Oligopyrimidine Tract Confers Translational Control on Top Mrnas in a Cell Type-and Sequence Context-Dependent Manner. Nucleic Acids Res 25: 995-1001Baruch K, Deczkowska A, David E, Castellano JM, Miller O, Kertser A, Berkutzki T, Barnett-Itzhaki Z, Bezalel D, Wyss-Coray T, et al (2014) Aging-induced type I interferon response at the choroid plexus negatively affects brain function. Science (80- ) 346: 89 LP - 93Baser A, Skabkin M, Kleber S, Dang Y, Gülcüler Balta GS, Kalamakis G, Göpferich M, Carvajal Ibañez D, Schefzik R, Lopez AS, et al (2019) Onset of differentiation is post-transcriptionally controlled in adult neural stem cells. Nature 566: 100-104Baser A, Skabkin M & Martin-Villalba A (2017) Neural Stem Cell Activation and the Role of Protein Synthesis. Brain Plast 3: 1-15Blanco S, Bandiera R, Popis M, Hussain S, Lombard P, Aleksic J, Sajini A, Tanna H, Cortés-Garrido R, Gkatza N, et al (2016) Stem cell function and stress response are controlled by protein synthesis. Nature 534: 335-340Blank T, Detje CN, Spieß A, Hagemeyer N, Brendecke SM, Wolfart J, Staszewski O, Zöller T, Papageorgiou I, Schneider J, et al (2016) Brain Endothelial- and Epithelial-Specific Interferon Receptor Chain 1 Drives Virus-Induced Sickness Behavior and Cognitive Impairment. Immunity 44: 901-912Blank T & Prinz M (2017) Type I interferon pathway in CNS homeostasis and neurological disorders. Glia 65: 1397-1406Carrasco-Garcia E, Moreno-Cugnon L, Garcia I, Borras C, Revuelta M, Izeta A, Lopez-Lluch G, de Pancorbo MM, Vergara I, Vina J, et al (2019) SOX2 expression diminishes with ageing in several tissues in mice and humans. Mech Ageing Dev 177: 30-36Daynac M, Morizur L, Chicheportiche A, Mouthon MA & Boussin FD (2016) Age-related neurogenesis decline in the subventricular zone is associated with specific cell cycle regulation changes in activated neural stem cells. Sci Rep 6: 1-10Delgado AC, Maldonado-Soto AR, Silva-Vargas V, Mizrak D, von Känel T, Tan KR, Paul A, Madar A, Cuervo H, Kitajewski J, et al (2021) Release of stem cells from quiescence reveals gliogenic domains in the adult mouse brain. Science (80- ) 372: 1205-1209Demerdash Y, Kain B, Essers MAG & King KY (2021) Yin and Yang: The dual effects of interferons on hematopoiesis. Exp Hematol 96: 1-12Dulken BW, Buckley MT, Navarro Negredo P, Saligrama N, Cayrol R, Leeman DS, George BM, Boutet SC, Hebestreit K, Pluvinage J V., et al (2019) Single-cell analysis reveals T cell infiltration in old neurogenic niches. NatureFaye MD, Graber TE & Holcik M (2014) Assessment of selective mRNA translation in mammalian cells by polysome profiling. J Vis Exp: 1-8Fernández-Fernández L, Comes G, Bolea I, Valente T, Ruiz J, Murtra P, Ramirez B, Anglés N, Reguant J, Morelló JR, et al (2012) LMN diet, rich in polyphenols and polyunsaturated fatty acids, improves mouse cognitive decline associated with aging and Alzheimer's disease. Behav Brain Res 228: 261-271Friend K, Brooks HA, Propson NE, Thomson JA & Kimble J (2015) Embryonic stem cell growth factors regulate eIF2α phosphorylation. PLoS One 10: 1-15Fulop T, Larbi A, Dupuis G, Page A Le, Frost EH, Cohen AA, Witkowski JM & Franceschi C (2018) Immunosenescence and inflamm-aging as two sides of the same coin: Friends or Foes? Front Immunol 8Gal-Ben-Ari S, Barrera I, Ehrlich M & Rosenblum K (2019) PKR: A kinase to remember. Front Mol Neurosci 11: 1-20Gingras AC, Raught B & Sonenberg N (2001) Regulation of translation initiation by FRAP/mTOR. Genes Dev 15: 807-826Glück S & Ablasser A (2019) Innate immunosensing of DNA in cellular senescence. Curr Opin Immunol 56: 31-36Guo Y, Yang K, Harwalkar J, Nye JM, Mason DR, Garrett MD, Hitomi M & Stacey DW (2005) Phosphorylation of cyclin D1 at Thr 286 during S phase leads to its proteasomal degradation and allows efficient DNA synthesis. Oncogene 24: 2599-2612Haas S, Hirche C, Schnell A, Sönmezer C, Langstein J, Wurzer S, Thier M, Blaszkiewicz S, Pilz F, Milsom MD, et al (2017) A Stem Cell-Based Epigenetic Memory Mediates Interferon Response-Heterogeneity within the Hematopoietic System. Blood 130: 634-634Hou Q, Huang J, Ayansola H, Masatoshi H & Zhang B (2021) Intestinal Stem Cells and Immune Cell Relationships: Potential Therapeutic Targets for Inflammatory Bowel Diseases. Front Immunol 11: 1-14Hsu PP, Kang SA, Rameseder J, Zhang Y, Ottina KA, Lim D, Peterson TR, Choi Y, Gray NS, Yaffe MB, et al (2011) The mTOR-regulated phosphoproteome reveals a mechanism of mTORC1-mediated inhibition of growth factor signaling. Science (80- ) 332: 1317-1322Huang S, Hendriks W, Althage A, Hemmi S, Bluethmann H, Kamijo R, Vilček J, Zinkernagel RM & Aguet M (1993) Immune response in mice that lack the interferon-γ receptor. Science (80- ) 259: 1742-1745Ivashkiv LB & Donlin LT (2014) Regulation of type i interferon responses. Nat Rev Immunol 14: 36-49Jia JJ, Lahr RM, Solgaard MT, Moraes BJ, Pointet R, Yang AD, Celucci G, Graber TE, Hoang HD, Niklaus MR, et al (2021) MTORC1 promotes TOP mRNA translation through site-specific phosphorylation of LARP1. Nucleic Acids Res 49: 3461-3489Jin WN, Shi K, He W, Sun JH, Van Kaer L, Shi FD & Liu Q (2021) Neuroblast senescence in the aged brain augments natural killer cell cytotoxicity leading to impaired neurogenesis and cognition. Nat Neurosci 24: 61-73Jolly A, Fanti AK, Kongsaysak-Lengyel C, Claudino N, Gräßer I, Becker NB & Höfer T (2022) CycleFlow simultaneously quantifies cell-cycle phase lengths and quiescence in vivo. Cell Reports Methods 2Kalamakis G, Brüne D, Ravichandran S, Bolz J, Ziebell F, Stiehl T, Catalá-Martinez F, Kupke J, Zhao S, Llorens-Bobadilla E, et al (2019) Quiescence Modulates Stem Cell Maintenance and Regenerative Capacity in the Aging Brain Article Quiescence Modulates Stem Cell Maintenance and Regenerative Capacity in the Aging Brain.Kim J & Guan KL (2019) mTOR as a central hub of nutrient signalling and cell growth. Nat Cell Biol 21: 63-71Kremer LPM, Cerrizuela S, Dehler S, Stiehl T, Weinmann J, Abendroth H, Kleber S, Laure A, El Andari J, Anders S, et al (2021) High throughput screening of novel AAV capsids identifies variants for transduction of adult NSCs within the subventricular zone. Mol Ther - Methods Clin Dev 23: 33-50Kroczynska B, Mehrotra S, Arslan AD, Kaur S & Platanias LC (2014) Regulation of interferon-dependent mRNA translation of target genes. J Interf Cytokine Res 34: 289-296Kyritsis N, Kaslin J, Freudenreich D, Iltzsche A, Kizil C, Brand M, Zocher S & Kroehne V (2012) Acute Inflammation Initiates the Regenerative Response in the Adult Zebrafish Brain. Science (80- ) 338: 1353-1356Lange C, Huttner WB & Calegari F (2009) Cdk4/CyclinD1 Overexpression in Neural Stem Cells Shortens G1, Delays Neurogenesis, and Promotes the Generation and Expansion of Basal Progenitors. Cell Stem Cell 5: 320-331Lekmine F, Sassano A, Uddin S, Smith J, Majchrzak B, Brachmann SM, Hay N, Fish EN & Platanias LC (2004) Interferon-γ engages the p70 S6 kinase to regulate phosphorylation of the 40S S6 ribosomal protein. Exp Cell Res 295: 173-182Lekmine F, Uddin S, Sassano A, Parmar S, Brachmann SM, Majchrzak B, Sonenberg N, Hay N, Fish EN & Platanias LC (2003) Activation of the p70 S6 kinase and phosphorylation of the 4E-BP1 repressor of mRNA translation by type I interferons. J Biol Chem 278: 27772-27780Liu GY & Sabatini DM (2020) mTOR at the nexus of nutrition, growth, ageing and disease. Nat Rev Mol Cell Biol 21: 183-203Liu HK, Belz T, Bock D, Takacs A, Wu H, Lichter P, Chai M & Schütz G (2008) The nuclear receptor tailless is required for neurogenesis in the adult subventricular zone. Genes Dev 22: 2473-2478Llorens-Bobadilla E, Zhao S, Baser A, Saiz-Castro G, Zwadlo K & Martin-Villalba A (2015) Single-Cell Transcriptomics Reveals a Population of Dormant Neural Stem Cells that Become Activated upon Brain Injury. Cell Stem Cell 17: 329-340Ma XM & Blenis J (2009) Molecular mechanisms of mTOR-mediated translational control. Nat Rev Mol Cell Biol 10: 307-318Marqués-Torrejón MÁ, Porlan E, Banito A, Gómez-Ibarlucea E, Lopez-Contreras AJ, Fernández-Capetillo Ó, Vidal A, Gil J, Torres J & Fariñas I (2013) Cyclin-dependent kinase inhibitor p21 controls adult neural stem cell expansion by regulating Sox2 gene expression. Cell Stem Cell 12: 88-100Mazewski C, Perez RE, Fish EN & Platanias LC (2020) Type I Interferon (IFN)-Regulated Activation of Canonical and Non-Canonical Signaling Pathways. Front Immunol 11: 1-13Mirzadeh Z, Doetsch F, Sawamoto K, Wichterle H & Alvarez-Buylla A (2010) The subventricular zone en-face: Wholemount staining and ependymal flow. J Vis Exp: 1-8Molofsky A V., Slutsky SG, Joseph NM, He S, Pardal R, Krishnamurthy J, Sharpless NE & Morrison SJ (2006) Increasing p16INK4a expression decreases forebrain progenitors and neurogenesis during ageing. Nature 443: 448-452Müller U, Steinhoff U, Reis LFL, Hemmi S, Pavlovic J, Zinkernagel RM & Aguet M (1994) Functional role of type I and type II interferons in antiviral defense. Science (80- ) 264: 1918-1921Nakamura T, Furuhashi M, Li P, Cao H, Tuncman G, Sonenberg N, Gorgun CZ & Hotamisligil GS (2010) Double-Stranded RNA-Dependent Protein Kinase Links Pathogen Sensing with Stress and Metabolic Homeostasis. Cell 140: 338-348Nicaise AM, Willis CM, Crocker SJ & Pluchino S (2020) Stem Cells of the Aging Brain. Front Aging Neurosci 12: 1-23Ogrodnik M, Zhu Y, Langhi LGP, Tchkonia T, Krüger P, Fielder E, Victorelli S, Ruswhandi RA, Giorgadze N, Pirtskhalava T, et al (2019) Obesity-Induced Cellular Senescence Drives Anxiety and Impairs Neurogenesis. Cell Metab 29: 1061-1077.e8Payea MJ, Anerillas C, Tharakan R & Gorospe M (2021) Translational Control during Cellular Senescence. Mol Cell Biol 41: 1-14Piazzi M, Bavelloni A, Faenza I & Blalock W (2020) Glycogen synthase kinase (GSK)-3 and the double-strand RNA-dependent kinase, PKR: When two kinases for the common good turn bad. Biochim Biophys Acta - Mol Cell Res 1867: 118769Pietras EM, Lakshminarasimhan R, Techner JM, Fong S, Flach J, Binnewies M & Passegué E (2014) Re-entry into quiescence protects hematopoietic stem cells from the killing effect of chronic exposure to type I interferons. J Exp Med 211: 245-262Platanias LC (2005) Mechanisms of type-I- and type-II-interferon-mediated signalling. Nat Rev Immunol 5: 375-386Potel CM, Lin MH, Heck AJR & Lemeer S (2018) Defeating major contaminants in Fe3- immobilized metal ion affinity chromatography (IMAC) phosphopeptide enrichment. Mol Cell Proteomics 17: 1028-1034Qin X, Jiang B & Zhang Y (2016) 4E-BP1, a multifactor regulated multifunctional protein. Cell Cycle 15: 781-786Ran FA, Hsu PD, Wright J, Agarwala V, Scott DA & Zhang F (2013) Genome engineering using the CRISPR-Cas9 system. Nat Protoc 8: 2281-2308Romero-Pozuelo J, Figlia G, Kaya O, Martin-Villalba A & Teleman AA (2020) Cdk4 and Cdk6 Couple the Cell-Cycle Machinery to Cell Growth via mTORC1. Cell Rep 31Sidrauski C, McGeachy AM, Ingolia NT & Walter P (2015) The small molecule ISRIB reverses the effects of eIF2α phosphorylation on translation and stress granule assembly. Elife 2015: 1-16Stanifer ML, Guo C, Doldan P & Boulant S (2020) Importance of Type I and III Interferons at Respiratory and Intestinal Barrier Surfaces. Front Immunol 11: 1-14Stemmer M, Thumberger T, Del Sol Keyer M, Wittbrodt J & Mateo JL (2015) CCTop: An intuitive, flexible and reliable CRISPR/Cas9 target prediction tool. PLoS One 10: 1-11Su Q, Wang S, Baltzis D, Qu LK, Raven JF, Li S, Wong AHT & Koromilas AE (2007) Interferons induce tyrosine phosphorylation of the eIF2α kinase PKR through activation of Jak1 and Tyk2. EMBO Rep 8: 265-270Su X, Yu Y, Zhong Y, Giannopoulou EG, Hu X, Liu H, Cross JR, Rätsch G, Rice CM & Ivashkiv LB (2015) Interferon-γ regulates cellular metabolism and mRNA translation to potentiate macrophage activation. Nat Immunol 16: 838-849Syedbasha M, Bonfiglio F, Linnik J, Stuehler C, Wüthrich D & Egli A (2020) Interferon-λ Enhances the Differentiation of Naive B Cells into Plasmablasts via the mTORC1 Pathway. Cell Rep 33Tahmasebi S, Amiri M & Sonenberg N (2019) Translational control in stem cells. Front Genet 10: 1-9Thoreen CC, Chantranupong L, Keys HR, Wang T, Gray NS & Sabatini DM (2012) A unifying model for mTORC1-mediated regulation of mRNA translation. Nature 485: 109-113Topacio BR, Zatulovskiy E, Cristea S, Xie S, Tambo CS, Rubin SM, Sage J, Kõivomägi M & Skotheim JM (2019) Cyclin D-Cdk4,6 Drives Cell-Cycle Progression via the Retinoblastoma Protein's C-Terminal Helix. Mol Cell 74: 758-770.e4Urbán N, Blomfield IM & Guillemot F (2019) Quiescence of Adult Mammalian Neural Stem Cells: A Highly Regulated Rest. Neuron 104: 834-848Vigo T, La Rocca C, Faicchia D, Procaccini C, Ruggieri M, Salvetti M, Centonze D, Matarese G & Uccelli A (2019) IFNβ enhances mesenchymal stromal (Stem) cells immunomodulatory function through STAT1-3 activation and mTOR-associated promotion of glucose metabolism. Cell Death Dis 10Wu X, Dao Thi VL, Huang Y, Billerbeck E, Saha D, Hoffmann HH, Wang Y, Silva LAV, Sarbanes S, Sun T, et al (2018) Intrinsic Immunity Shapes Viral Resistance of Stem Cells. Cell 172: 423-438.e25Young SK & Wek RC (2016) Upstream open reading frames differentially regulate genespecific translation in the integrated stress response. J Biol Chem 291: 16927-16935Zhang C, Cheng N, Qiao B, Zhang F, Wu J, Liu C, Li Y & Du J (2020) Age-related decline of interferon-gamma responses in macrophage impairs satellite cell proliferation and regeneration. J Cachexia Sarcopenia Muscle 11: 1291-1305Zhu X, Chen Z, Shen W, Huang G, Sedivy JM, Wang H & Ju Z (2021) Inflammation, epigenetics, and metabolism converge to cell senescence and ageing: the regulation and intervention. Signal Transduct Target Ther 6: 1-29Zismanov V, Chichkov V, Colangelo V, Jamet S, Wang S, Syme A, Koromilas AE & Crist C (2016) Phosphorylation of eIF2α is a Translational Control Mechanism Regulating Muscle Stem Cell Quiescence and Self-Renewal. Cell Stem Cell 18: 79-90

Expanded View Figures