阅读说明：本技术 针对细胞衰老的生物标志物 (Biomarkers for cellular senescence ) 是由 马可·德马利亚 亚丽珍达·赫南德斯-瑟古拉 于 2018-03-09 设计创作，主要内容包括：本发明涉及生物标志物及其用途,具体涉及提供关于细胞是否衰老的重要指示的蛋白或mRNA的集合。提供了包含6种或更多种多肽或它们的编码mRNA的生物标志物组作为针对细胞衰老的生物标志物集合的用途,其中所述组至少包含生物标志物TSPAN13、GDNF、C2CD5、SUSD6、BCL2L2、PLK3,或它们的变体或片段。还提供了用于检测衰老细胞的衰老细胞检测试剂盒,以及用于杀死衰老细胞的药物缀合物。(The present invention relates to biomarkers and uses thereof, in particular to a collection of proteins or mrnas that provide an important indication as to whether a cell is senescent. There is provided the use of a biomarker panel comprising 6 or more polypeptides or their encoding mrnas as a set of biomarkers for cellular senescence, wherein the panel comprises at least the biomarkers TSPAN13, GDNF, C2CD5, SUSD6, BCL2L2, PLK3, or variants or fragments thereof. Also provided are senescent cell detection kits for detecting senescent cells, and drug conjugates for killing senescent cells.)

1. Use of a biomarker panel comprising 6 or more polypeptides or their encoding nirnas as a set of biomarkers for cellular senescence, wherein the panel comprises at least the biomarkers TSPAN13, GDNF, C2CD5, SUSD6, BCL2L2, PLK3, or variants or fragments thereof.

2. The use of claim 1, wherein the collection further comprises one or more biomarkers selected from the group consisting of: CTLN, FAM214B, PATZ₁PLXNA3, STAG1, tolllip, TRDMT1, ZBTB7A, ARID2, B4GALT7, CHMP5, CREBBP, DDA1, DYNLT3, EFNB3, ICE1, MEIS1, NOL3, PCIF1, PDLIM4, PDS5B, RAI14, RHNO1, SCOC, SLC16A3, SMO, SPIN4, TAF13, TMEM 6387 87B, UFM1, and hiznt 1, or variants or fragments thereof.

3. The use of claim 1 or 2, wherein the collection comprises the biomarkers TSPAN13, GDNF, C2CD5, SUSD6, BCL2L2, PLK3, and one or both of DYNLT3 and PLXNA3, or variants or fragments thereof.

4. The use of claim 3, wherein the collection comprises or consists of the biomarkers TSPAN13, GDNF, C2CD5, SUSD6, BCL2L2, PLK3, DYNLT3 and PLXNA3, or variants or fragments thereof.

Use of a TSPAN13 polypeptide or its encoding mRNA, or a variant or fragment thereof, as a biomarker for cellular senescence.

6. A method of detecting senescent cells in a test sample, the method comprising detecting expression of a set of biomarkers for at least the senescent cells of any one of claims 1 to 5 in the sample, wherein altered expression levels of at least one of the biomarkers, relative to the expression levels detected in a reference sample, is indicative of the presence of senescent cells in the sample.

7. The method of claim 6, wherein an increased expression level of TSPAN13, GDNF, C2CD5, PLXNA3, SUSD6, BCL2L2, and/or PLK3, or a variant or fragment thereof, relative to the expression level detected in a reference sample is indicative of the presence of senescent cells in the sample.

8. The method of claim 6 or 7, wherein the test sample is a body sample taken from a test subject, preferably wherein the sample comprises blood, plasma, serum, spinal fluid, urine, sweat, saliva, tears, breast aspirate, prostatic fluid, semen, vaginal secretions, stool, cervical scrape, cells, amniotic fluid, ocular fluid, mucus, moisture in the breath, animal tissue, cell lysate, tumor tissue, hair, skin, buccal scrape, nails, bone marrow, cartilage, infectious proteins, bone meal, earwax, or a combination thereof.

9. The method of any one of claims 6 to 8, wherein the test subject is a laboratory animal or a human.

10. A senescent cell detection kit for detecting senescent cells in a sample, the kit comprising means for detecting the presence of at least the set of biomarker polypeptides or nirnas of any one of claims 1 to 4 in a sample from a test subject.

11. The kit of claim 10, wherein the kit comprises at least one control or reference sample, preferably wherein the kit comprises a negative control and/or a positive control.

12. A drug conjugate for killing senescent cells, the conjugate comprising: (i) a senescent cell targeting agent configured to specifically target and bind to at least one senescent cell biomarker selected from the group consisting of: TSPAN13, GDNF, FAM214B, PLXNA3, SUSD6, tollid, ZBTB7A, B4GALT7, BCL2L2, CHMP5, DDA1, DYNLT3, NOL3, PDLIM4, PLK3, RAI14, SCOC, SLC16a3, TAF13, TMEM87B, UFM1, and ZNHIT1, preferably wherein the targeting agent is capable of binding to a biomarker selected from the group consisting of: TSPAN13, PLXNA3, SUSD6, GDNF, FAM214B, tollid and ZBTB7A, and (ii) a cytotoxic agent that kills the bound senescent cells.

13. The drug conjugate of claim 12, comprising a targeting agent configured to specifically target and bind to TSPAN13 when in use.

14. The drug conjugate of claim 12 or 13, wherein the targeting agent is an antibody or antigen-binding fragment thereof, an aptamer, a plastic antibody, or a small molecule.

15. The drug conjugate of any one of claims 12-14, wherein the cytotoxic agent is a lytic agent, a radioisotope, a toxin, or a toxic peptide, preferably a lytic agent.

16. The drug conjugate of claim 15, wherein the lytic agent is: (a) inhibitors of Bcl-2 anti-apoptotic protein family members; (b) MDM2 inhibitors; or (c) an Akt specific inhibitor, preferably an inhibitor of one or more BCL-2 anti-apoptotic protein family members, wherein said inhibitor inhibits at least Bcl-xL, more preferably selected from the group consisting of ABT-263, ABT-737, WEHI-539 and A-l 155463.

17. A drug conjugate according to any one of claims 12-16 for use as a medicament.

18. The drug conjugate of any one of claims 12-17 for use in the treatment, delay of progression, prevention or amelioration of an age-related disease, preferably wherein the age-related disease is atherosclerosis, cardiovascular disease, cancer, arthritis, glaucoma, cataract, osteoporosis, type 2 diabetes, hypertension, alzheimer's disease or other types of dementia.

19. A pharmaceutical composition comprising the drug conjugate of any one of claims 12-16 and a pharmaceutically acceptable vehicle.

20. A method for treating a senescence-associated disease or disorder, comprising administering to a subject in need thereof a pharmaceutical composition comprising a therapeutically effective amount of the drug conjugate of any one of claims 12-16 that selectively kills senescent cells over non-senescent cells.

Drawings

FIG. 1 meta-analysis of senescent fibroblast transcriptomics. And (4) experimental design. Seven RNA-seq datasets, including three types of senescent and six different fibroblast lines, were used to establish stimulus-specific characteristics and general characteristics of senescent fibroblasts independent of stimulus. Each feature was established using three methods: negative binomial Generalized Linear Model (GLM), Fisher, and negative-positive p-value combinations. Only genes whose p-value calculated by these three methods is 0.01 and whose expression is unchanged or in the opposite direction of rest were included in each feature. The characteristics of ionizing radiation induced aging (IRIS) were established with only negative binomial GLM, as only one data set was available. The number of genes comprising each feature is shown: 1721 genes characteristic of Replicative Senescence (RS), 1586 genes characteristic of oncogene-induced senescence (OIS), 2688 genes characteristic of ionizing radiation-induced senescence (IRIS) and 726 genes characteristic of senescence in fibroblasts not associated with stimulation.

FIG. 2 characteristics of core senescence-associated features. A. And (4) experimental design. RNA-seq datasets obtained from pilot studies of melanocytes, keratinocytes and astrocytes were compared to senescence characteristics of fibroblasts. The intersections of genes differentially expressed (p-value < ═ 0.01) in all data sets are shown in the petal plots. B. Heatmap of 37 genes characteristic of senescence core. The figure shows the log base 2 of the fold change for each cell type relative to proliferating cells. C. Gene Ontology (GO) entries enriched in core senescence signature. The figure shows enriched GO entries in the up-regulated (red) and down-regulated (blue) genes of the signature. Bars represent the base 10 logarithm of the p-value. D. Pathways enriched in the core characteristics of senescence. The pathways enriched in genes within core senescence signature (B) are listed together with their corresponding p-values and sources.

FIG. 3 temporal dynamics of senescent transcriptomes. A. And (4) experimental design. Fibroblasts (HCA-2, yellow), melanocytes (red) and keratinocytes (magenta) were exposed to Ionizing Radiation (IR) and RNA was harvested after 4, 10 or 20 days.

Transcriptomes of different cell types and intervals after senescence induction were obtained by RNA-seq. Time-point signatures with genes differentially expressed in all three cell types (p-value < 0.01) and a shared IR-induced senescence (IRIS) signature (p-value < 0.01) with genes shared by all cell types and time points were generated. B. Heat maps showing the SASP kinetics for each cell type. Known SASP factors that are significantly differentially expressed in each cell type at least one time point are shown. The heat map shows the log base 2 of fold change relative to proliferating cells after each irradiation. Quiescence was measured only on fibroblasts. The purple arrows highlight MMP1, the only factor of SASP that is normally regulated at day 10 and day 20 in all cell types.

FIG. 4 dynamic changes in gene expression in core senescence profiles. Each panel shows one of the 37 genes in the core signature of senescence at the indicated points before and after irradiation. All genes showed dynamic time behavior at the time points tested (day 0 (proliferation), day 4, day 10 and day 20 after irradiation). Notably, all genes showed similar trends in the three cell types tested: fibroblasts (yellow), melanocytes (red) and keratinocytes (magenta). Genes in red correspond to those that reached significance (p-value < ═ 0.01) at all time points tested.

FIG. 5 Gene expression of senescence markers normalized to tubulin. Senescence was confirmed by SA-bgal (data not shown) and at least one established marker of senescence (down-regulation of LMNB1 or up-regulation of p21) in all samples. Tubulin was used as a reference gene to calculate the Δ Ct value. A) Expression of LMNB1 and p21 in proliferating (Ctrl) and Irradiated (IR) BJ fibroblasts. B) Expression of LMNB1 and p21 in proliferating (Ctrl) and doxorubicin-treated (Doxo) keratinocytes. C) Expression of LMNB1 and p21 in proliferating (Ctrl) and HCA2 fibroblasts 4 or 10 days post irradiation (d 4 and d10, respectively). D) Expression of LMNB1 and p21 in proliferating (Ctrl), Irradiated (IR) and Replicative Senescence (RS) melanocytes.

FIG. 6 Gene expression of preselected genes for senescence signature normalized to tubulin in BJ fibroblasts. Gene expression of different biomarker genes within the senescence signature of the invention was measured in proliferating (Ctrl) and irradiated (IR, day 10 post irradiation) BJ fibroblasts by real-time PCR. The Δ Ct values were calculated using tubulin according to the method developed by Livak et al (2001.Methods25 (4)). Each condition included three biological replicates, each run in technical replicates. Error bars show the standard error of the mean. Notably, the results were not always statistically significant (data not shown).

FIG. 7 Gene expression of preselected biomarkers of senescence signature normalized to tubulin in HCA2 fibroblasts. Gene expression of different biomarker genes within the senescence signature was measured by real-time PCR in proliferating (Ctrl) and irradiated HCA2 fibroblasts 4 or 10 days (d 4 and d10, respectively). The Δ Ct values were calculated using tubulin according to the method developed by Livak et al (2001.Methods25 (4)). Each condition included 3 biological replicates, each run in technical replicates. Error bars show the standard error of the mean. Notably, the results were not always statistically significant (data not shown).

FIG. 8 Gene expression of preselected biomarkers of senescence signature normalized against tubulin in keratinocytes. Gene expression of different biomarker genes within the senescence signature in proliferating (Ctrl) and doxorubicin-treated (Doxo) keratinocytes was measured by real-time PCR. The Δ Ct values were calculated using tubulin according to the method developed by Livak et al (2001.Methods25 (4)). Each condition included two biological replicates, each run in technical replicates. Error bars show the standard error of the mean. Notably, the results were not always statistically significant (data not shown).

FIG. 9 Gene expression of preselected biomarkers of senescence signature normalized to tubulin in melanocytes. Gene expression of different biomarker genes within the senescence signature in proliferating (Ctrl), Irradiated (IR) or Replicative Senescence (RS) melanocytes was measured by real-time PCR. The Δ Ct values were calculated using tubulin according to the method developed by Livak et al (2001.Methods25 (4)). Each condition included 3 biological replicates, each run in technical replicates. Error bars show the standard error of the mean. Notably, the results were not always statistically significant (data not shown).

FIG. 10 Gene expression of senescence markers normalized to actin. Senescence was confirmed by SA-bgal (data not shown) and at least another senescence marker (down-regulation of LMNB1 or up-regulation of p21) in all samples. Actin was used as a reference gene to calculate the Δ Ct value. A) Expression of LMNB1 and p21 in proliferating (Ctrl) and Irradiated (IR) BJ fibroblasts. B) Expression of LMNB1 and p21 in proliferating (Ctrl) and doxorubicin-treated (Doxo) keratinocytes. C) Expression of LMNB1 and p21 in proliferating (Ctrl) and HCA2 fibroblasts 4 or 10 days post irradiation (d 4 and d10, respectively).

FIG. 11 Gene expression of preselected genes for senescence characteristics normalized to actin in BJ fibroblasts. Gene expression of different genes within the senescence signature was measured by real-time PCR in proliferating (Ctrl) and irradiated (IR, day 10 post irradiation) BJ fibroblasts.

FIG. 12 Gene expression of preselected genes for senescence characteristics normalized to actin in HCA2 fibroblasts. Gene expression of different genes within the senescence signature was measured by real-time PCR in proliferating (Ctrl) and irradiated HCA2 fibroblasts 4 or 10 days (d 4 and d10, respectively).

FIG. 13 Gene expression of preselected genes for senescence characteristics normalized to actin in keratinocytes. Gene expression of different genes within the senescence signature was measured in proliferating (Ctrl) and doxorubicin-treated (Doxo) keratinocytes by real-time PCR.

FIG. 14 Principal Component Analysis (PCA) of the Δ Ct values of preselected genes enables differentiation between senescent and proliferating cells. These genes include: BCL2L2, C2CD5 (primer pair amplified variants 1, 2, and 6), DYNLT3, GDNF (primer pair amplified variant 1), MTCYB, PLK3, PLXNA3, SUSD6, and TSPAN 13. A) PCA plots of propagated (Ctrl) and Irradiated (IR) BJ fibroblasts using the selected genes. B) PCA plots of proliferating (Ctrl) and doxorubicin-treated (Doxo) keratinocytes using the selected genes. C) PCA plots of HCA2 fibroblasts at day 4 and day 10 (d 4 and d10, respectively) after proliferation (Ctrl) and irradiation using the selected genes. D) PCA plots of proliferating (Ctrl), Irradiated (IR) and Replicative Senescent (RS) melanocytes using selected genes.

Figure 15. only a set of 6 biomarkers ("minimal core signature") is necessary and sufficient to distinguish between senescent and proliferating cells in different cell types. A) The contribution of each biomarker gene on principal component 1 (X-axis) on figure 11 to sample isolation was calculated for each sample set. For each cell type (BJ ═ BJ fibroblasts, HCA2 ═ HCA2 fibroblasts, Ker ═ keratinocytes, Mel ═ melanocytes), the biomarker score with the higher contribution was "1" and those with the lowest contribution were scored "9". The total score for each biomarker was calculated, with the biomarker having a higher contribution in all samples at "1" and the biomarker having the lowest contribution in all samples at "9". Panels B) -E) show new PCA plots established using 6 final genes for each sample: GDNF (primer pair amplification variant 1), TSPAN13, BCL2L2, PLK3, SUSD6, and C2CD5 (primer pair amplification variants 1, 2, and 6). B) PCA plot of core transcriptional markers of senescence of proliferating (Ctrl) versus Irradiated (IR) BJ fibroblasts. C) PCA plot of core transcriptional markers of senescence for proliferating (Ctrl) versus doxorubicin-treated (Doxo) keratinocytes. D) PCA plot of the core transcriptional marker of senescence for proliferating (Ctrl) versus HCA2 fibroblasts at day 4 or day 10 post irradiation (d 4 and d10, respectively). E) PCA plot of core transcriptional markers of senescence for proliferating (Ctrl), Irradiated (IR) and Replicative Senescence (RS) melanocytes.

FIG. 16 expression of TSPAN13mRNA was upregulated in senescent fibroblasts. Senescence of human skin fibroblasts BJ was induced by doxorubicin (Doxo), Ionizing Radiation (IRIS), hydrogen peroxide (OSIS) or replicative depletion (RS). Proliferating (Prolif) or quiescent (Quiesc) cells were used as controls. FIG. A: percentage of cells with senescence-associated activation of beta-galactosidase (SA-bgal). And B: percentage of cells with active DNA synthesis (EdU, proliferating reporter). And (C) figure: levels of TSPAN13mRNA measured by qPCR after normalization of tubulin mRNA levels. N-3 independent experiments. P is 0.05, p is 0.01.

FIG. 17 expression of TSPAN13 protein was upregulated in senescent fibroblasts. The induction of BJ senescence in human skin fibroblasts by Ionizing Radiation (IR). After 9 days, Control (CTRL) or IR cells were stained with antibody against TSPAN13 (green) and counterstained with DAPI to reveal nuclei (blue). FIG. 18 expression of TSPAN13 protein was upregulated in senescent fibroblasts. Human skin fibroblasts BJ were induced to undergo aging by Ionizing Radiation (IR). After 9 days, Control (CTRL) or IR cells were stained with an antibody against TSPAN13, and signal intensity was measured via flow cytometry. FIG. A: dot plots of fluorescence intensity for 2 populations. And B: quantification of data in panel a.

Figure 19 expression of TSPAN13 protein was upregulated in senescent fibroblasts. Human skin fibroblast WI38 senescence was induced by doxorubicin or palbociclib, and the induction of senescence (compared to control cells) as demonstrated by the percentage of cells with senescence-associated β -galactosidase (SA-bgal, panel a) activation was reported, as well as the induction of senescence (compared to control cells) as demonstrated by the percentage of cells with active DNA synthesis (EclU, panel B) was shown. Cells were stained with antibodies to TSPAN13 and signal intensity was measured via flow cytometry. And (C) figure: dot plots of fluorescence intensity for 3 populations. FIG. D: and (4) quantifying.

Figure 20.TSPAN13 positive cells show increased expression levels of other senescence markers. Human skin fibroblast BJ senescence was induced by Ionizing Radiation (IR), stained with an antibody against TSPAN13, and signal intensity was measured via flow cytometry. The graph in panel (a) shows the change in TSPAN13 expression in IR cells (red clock) compared to control cells (green clock). Panel (B) shows IR cells with high (IR + TSPAN13) or low (IR-TSPAN13) expression of TSPAN13 sorted in 2 separate tubes and RNA isolated. TSPAN13 high expressing cells showed high levels of other senescence markers p16 and p21 measured by qPCR and normalized to tubulin when compared to TSPAN13 low expressing cells.

Experimental part

Materials and methods

Cell lines and cultures

Human foreskin fibroblasts HCA2 were obtained from the laboratory of o.pereira-Smith (University of Texas Health Science Center, San Antonio); human foreskin fibroblasts BJ were purchased from ATCC (Cat: CRL-2522); MEFs were generated from day 13.5 embryos as previously described (Demaria 2010); mouse primary cutaneous microvascular endothelial cells were purchased from Cellbologics (Cat: C57-6064).

All cells were cultured in 5% oxygen for at least 4 doublings before use. Fibroblasts were cultured in dmem (thermo fisherscientific) rich in 10% fetal bovine serum (FBS, GE Healthcare Life Sciences) and 1% penicillin/streptomycin (Lonza). Endothelial cells were cultured in endothelial cell growth medium (ATCC).

Quiescence was induced by culturing the cells in DMEM supplemented with 0.2% FBS for 48 hours. For replicative senescence, cells were passaged (re-cultured at 30% -40% density until they reached 70% -80% confluence) until proliferation ceased (-65 population doublings for BJ cells). For oxidative stress-induced senescence, cells were treated with 200uM hydrogen peroxide (Sigma Aldrich) for 2h, then the drug was removed and cultured in fresh DMEM supplemented with 10% FBS. Treatments were repeated on days 0, 3 and 6, with media being refreshed every 2 days, and cells harvested on day 10 after the first treatment. Adriamycin (Tebu-bio) was used at 250nM for 24 h. The medium was replaced with DMEM supplemented with 10% FBS and refreshed every 2 days. Cells were harvested on day 7 post treatment. For radiation-induced senescence, cells were exposed to 10Gy γ -irradiation with a 137 cesium source and the medium was refreshed every 2 days.

Cells were harvested on day 10 post-irradiation for most experiments and validation. For the time series, cells were harvested at days 4, 10 and 20 post irradiation.

SA-beta galactosidase assay

Cells were seeded in 24-well plates and fixed in glutaraldehyde/formaldehyde (2%/2%) for 10-15min and stained with X-Gal solution overnight using a commercial kit (Biovision). Cells were counterstained with 1. mu.g/ml 4', 6-diamidino-2-phenylindole (DAPI, Sigma-Aldrich, D9542) for 20 min. Images were taken at 100 × magnification and the number of cells was counted by the software ImageJ (www.rsbweb.nih.gov/ij /). Positive cells were scored manually. Samples were run in triplicate, and at least 100 cells were counted per replicate.

EdU staining

Cells were cultured for 24h in the presence of EdU, then fixed and stained using a commercial kit (Click-iT EdU Alexa Fluor488 imaging kit; Thermo Fisher Scientific). Images were taken at 400 Xmagnification and quantified using ImageJ (www.rsbweb.nih.gov/ij /). Samples were run in triplicate, and at least 100 cells were counted per replicate.

Real-time PCR

Total RNA was prepared using the isolation II Rna Mini kit (the Isolate II Rna Mini kit, Bioline). 255-500 ng of RNA was reverse transcribed using a kit (Applied Biosystems). The qRT-PCR reaction was performed using the Universal Probe Library System (Roche) and the SENSASast Probe kit (Bioline) as described [43 ]. Tubulin was used for normalization of CT values. All samples were run repeatedly using a technique using 2-3 biological replicates. Unpaired two-tailed student t-test was used to determine statistical significance based on Δ CT values. P values of 05 or less are considered significant.

RNAseq

Cells were prepared for RNA extraction using RNAeasy mini kit (Invitrogen). Samples were treated with Qiasol lysis buffer and total RNA was isolated using the qiatube automaton according to the manufacturer's instructions (Invitrogen). RNA was quantified using NanoDrop and RNA quality was measured using a BioAnalyzer chip (Agilent). Purified RNA samples were sent to the University of minnesota biomedical Genomics Center (the University of minnesotaBiomedical Genomics Center) for library preparation (poly a enrichment) and Illumina HiSeq RNA sequencing according to the manufacturer's protocol (Illumina). For an insert size of-200 bp, size selection for 50bp paired-end sequencing was performed and sequencing was performed with > 220M reads per lane of the HiSeq2500 flow cell. The mean quality score of the completed runs was >30 in all samples, with an average number of reads of >1000 ten thousand reads per pooled sample. The raw data has been stored in the ArrayExpress database (https:// www.ebi.ac.uk/ArrayExpress/; accession number E-MTAB-5403).

Public data set

Tables Si and S4 are a summary of the common data set used and the samples. Raw data from a common data set is obtained from a "GEO repository". 6 public datasets of transcriptomes of senescent fibroblasts were included: 1) alspach et al, 2014[16] ("GSE 56293"), use RS of BJ cells to study SASP induction; 2) dikovskaya et al, 2015[17] ("GSE 70668"), use IMR90 cells to study multinucleation in OIS (induced by Ras) and use cells synchronized in mitosis; 3) herranz et al, 2015[18] ("GSE 61130"), studied SASP in OIS (induced by Ras) in IMR90 cells; 4) marthandan et al, 2015[20] ("GSE 63577"), used MRC-5 and HFF cells to study the effect of rotenone at different population doubling levels, and we used only HFF cells at the first time point (proliferation) and last time point; 5) marthandan et al, 2016[19] ("GSE 64553"), used 5 fibroblast lines (BJ, WI-38, IMR90, HFF and MRC-5) to study RS; 6) rai et al, 2014[21] ("GSE 53356"), used IMR90 cells to study the chromatin landscape of RS. A common data set ("GSE 58910") generated by Crowe et al, 2016[30] of OSIS in astrocytes was studied for the core features of senescence common to different cell types.

Quality control and alignment of transcriptome datasets

The raw data is downloaded as a fastq file using SRA Toolkit 2.6.2. All samples (including our own samples) were quality controlled using FastQC software vo.11.5 and low quality reads were discarded (mean mass: < 20). Trimming of the ends was performed using trimmatic 0.36 if necessary. The samples were aligned to GRCh38 genome using STAR-2.5.1b aligner and raw read count table was obtained directly from STAR output. Only genes annotated as protein-encoding were included in the analysis.

Meta-analysis of fibroblasts

Data heterogeneity was assessed using PCA plots of log-transformed normalized counts of protein-encoding genes. For meta-analysis of specific stimulation and fibroblast senescence characteristics, we used three methods: negative binomial Generalized Linear Model (GLM), Fisher p-value combination, and anti-positive p-value combination. The first method used R-package DESeq2 for differential expression analysis, which used senescence versus proliferation as the primary variable. In the case where more than one cell type is used, the cell type is included as a covariate.

Two other methods used R-package meta-RNAseq. First, differential expression analysis was performed on each data set using the DESeq2 package, and p values were combined by two methods: fisher and abnormal. Genes with p-value of 0.01 adjusted by multiple tests (using the Benjamini-Hochberg program) in negative binomial GLM and p-value of 0.01 in combination in the other two methods were included in the corresponding characteristics. After meta-analysis was completed, genes as senescence markers that were also differentially regulated (adjusted p-value of 0.01 and signs of fold change in the same direction as senescence) in the resting samples were removed. Enriched pathways and gene ontology entries in differentially expressed genes in fibroblast senescence signatures were assessed using the online tool "Over-representation analysis (http:// cpdb. molgen. mpg. de /).

Core senescence characteristic common to different cell types

Differential expression analysis was also performed on each dataset separately with DESeq2 and the list of differentially expressed genes was compared to senescence characteristics of fibroblasts without pooling the p-values. Only genes with multiple test-adjusted p-values of < ═ 0.01 (negative binomial GLM method) in each dataset and fibroblast signature were included in the core senescence signature.

Drawing (A)

All figures were made using the following R-package: "pheatmap", "ggplot 2", "ggfortify", "rcolrbrewer" and "venn diagram (VennDiagram)".

Example 2: the smallest core features of senescent cells are identified.

This example describes the analysis of gene expression of different biomarker genes within a senescence signature to obtain a "minimal core senescence signature" comprising a set of biomarkers necessary and sufficient to distinguish senescent cells from non-senescent cells.

To this end, the set of pre-selected biomarkers identified in example 1 in4 different cell types undergoing senescence was measured by real-time PCR:

HCA2 fibroblasts: control vs. 4 th and 10 th day after irradiation

BJ fibroblasts: control versus irradiation

Melanocytes: controls versus irradiated and replicative aged (in triplicate)

Keratinocyte: control vs doxorubicin-treated (in duplicate)

Materials and methods

Cell lines and culture.Human foreskin fibroblasts BJ, human neonatal melanocytes and human neonatal keratinocytes were purchased from ATCC (Cat: CRL-2522, PCS-200-. BJ fibroblasts were cultured in DMEM medium (Thermo FisherScientific) rich in 10% fetal bovine serum (FBS, ge healthcare Life Sciences) and 1% penicillin/streptomycin (Lonza). Keratinocytes were cultured in Cnt-Prime epithelial medium (CellnTec, Cnt-PR) without antibiotic addition. Melanocytes were cultured in RPMI medium enriched with 10% fetal bovine serum and 1% penicillin/streptomycin, supplemented with 200nM 12-O-tetradecanoylphosphatel 13-acetate (TPA, Sigma-Aldrich), 200nM cholera toxin (Sigma-Aldrich), 10nM endothelin 1(Sigma-Aldrich), and 10ng/ml human stem cell factor (Peprotech). All cells were cultured at 5% oxygen, 5% CO2 and 37 ℃ and tested for mycoplasma infection periodically.

And (4) preparing a sample.For ionizing radiation induced aging(IRIS), use of¹³⁷The cesium source was gamma-irradiated to the cells at a dose of 10Gy and the medium was refreshed every 2 days. Cells were harvested at day 4 and/or day 10 post irradiation. For Replicative Senescence (RS), cells were propagated in culture for 4 months (re-cultured at 30-40% density each time they reached 70-80% confluence) until they stopped growing (-PD 65). Doxorubicin (Tebu-bio) was used at a concentration of 250nM for 24 hours. Cells were washed once with their respective media, then new media was added and refreshed every two days. Cells were harvested on day 7 post treatment. The cells were stimulated to produce proliferation controls for each condition with the same PD as the treated sample or treated with vehicle (PBS) in the case of doxorubicin.

Senescence was confirmed by the SA-. beta.gal assay.Cells were seeded in 24-well plates, fixed in a mixture of glutaraldehyde and formaldehyde (2%/2%) for 3-5 minutes, and stained with X-Gal solution overnight using a commercial kit (Biovision). Cells were counterstained with a solution of 1pg/ml 4', 6-diamidino-2-phenylindole (DAPI, Sigma-Aldrich, D9542) for 20 min. Images were taken at 100 Xmagnification and the number of cells was counted by the software ImageJ (www.rsbweb.nih.gov/ij /). The number of positive cells was counted manually. Data for SA-bgal staining are not shown.

And (5) carrying out real-time PCR.Total RNA was prepared using an isolation II Rna mini kit (Bioline). 100-500ng of RNA was reverse transcribed into cDNA using a kit (applied biosystems). The qRT-PCR reaction was performed according to the manufacturer's instructions as using the universal probe library system (Roche) and SENSIFast probe kit (Bioline). Expression of tubulin or actin was used to normalize the expression of CT values. The technique was repeated and the samples were run in 2-3 biological replicates. Unpaired two-tailed student t-test was used to determine statistical significance based on Δ CT values.

Principal Component Analysis (PCA):genes for principal component analysis were preselected based on the reproducibility of changes in the expression between proliferating cells and senescent cells in the qPCR results. Genes that follow the same trend in most samples as predicted by the original analysis (based on RNAseq results) were used to establish PCA profiles. These genes include: BCL2L2, C2CD5 (primer set amplification variant 1, DNA fragment),2 and 6), DYNLT3, GDNF (primer pair amplification variant 1), MTCBB, PLK3, PLXNA3, SUSD6 and TSPAN 13. For each sample set, the contribution of each gene on principal component 1 (X-axis) to the sample separation was calculated. The gene with the higher contribution was scored as "1", and the gene with the lowest contribution was scored as "9". A list of all samples for analysis was established and the total score based on each gene for all samples was calculated with the gene having the higher contribution in all samples at "1" and the gene having the lowest contribution in all samples at "9". Finally, the last (7, 8 and 9) 3 genes scored were discarded. A new PCA plot was created using 6 final genes: GDNF (primer pair amplification variant 1), TSPAN13, BCL2L2, PLK3, SUSD6, and C2CD5 (primer pair amplification variants 1, 2, and 6).

List of primers used:

materials:

results

Figure 6 demonstrates senescence of each cell sample under the study conditions by analyzing at least established senescence markers (down-regulation of LMNB1 or up-regulation of p21) normalized to tubulin.

Fig. 7-9 show gene expression of preselected biomarker genes for senescence signature normalized to tubulin in BJ fibroblasts (fig. 7), HCA2 fibroblasts (fig. 8), and keratinocytes (fig. 9) as measured by real-time PCR. The Δ Ct values were calculated using tubulin according to the method developed by Livak et al (2001.Methods25 (4)). Each condition included 3 biological replicates, each run in technical replicates. Error bars show the standard error of the mean. Notably, the results were not always statistically significant (data not shown).

Figure 10 demonstrates senescence of each cell sample under the study conditions by analyzing at least established senescence markers (down-regulation of LMNB1 or up-regulation of p21) normalized to actin.

Fig. 11-13 show gene expression of preselected biomarker genes for senescence signature normalized to actin in BJ fibroblasts (fig. 11), HCA2 fibroblasts (fig. 12), and keratinocytes (fig. 13) as measured by real-time PCR. The Δ Ct values were calculated using tubulin according to the method developed by Livak et al (2001.Methods25 (4)). Each condition included 3 biological replicates, each run in technical replicates. Error bars show the standard error of the mean. Notably, the results were not always statistically significant (data not shown).

Thereafter, biomarker genes for the principal component analysis were preselected based on the reproducibility of changes in the expression between proliferating cells and senescent cells in the qPCR results. Genes that follow the same trend (up-or down-regulated, independent of statistical significance) in most samples as predicted by the original analysis (based on RNAseq results) were used to establish PCA plots of Δ Ct values normalized to tubulin. These biomarkers include BCL2L2, C2CD5 (primer pair amplified variants 1, 2, and 6), DYNLT3, GDNF (primer pair amplified variant 1), MTCYB, PLK3, PLXNA3, SUSD6, and TSPAN 13. See fig. 14, which shows PCA plots of BJ fibroblasts, keratinocytes, HCA fibroblasts, and melanocytes.

Finally, the contribution of each gene on principal component 1 (X-axis) on fig. 14 to the sample separation was analyzed by calculating each set of samples, identifying a set of only 6 biomarker genes (minimal core features). For each cell type (BJ ═ BJ fibroblasts, HCA2 ═ HCA2 fibroblasts, Ker ═ keratinocytes, Mel ═ melanocytes), the biomarker gene with the highest contribution was scored as "1" and the gene with the lowest contribution was scored as "9". The total score for each gene was calculated, with the gene having the higher contribution in all samples at "1" and the gene having the lowest contribution in all samples at "9". The last (red) 3 genes scored were discarded. This resulted in a set comprising the biomarkers TSPAN13, GDNF, C2CD5, SUSD6, BCL2L2, and PLK3 (see fig. 15).