DIPPER, a Spatiotemporal Proteomics Atlas of Human Intervertebral Discs

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DIPPER, a spatiotemporal proteomics atlas of human intervertebral discs for exploring ageing and degeneration dynamics Vivian Tam1,2†, Peikai Chen1†‡, Anita Yee1, Nestor Solis3, Theo Klein3§, Mateusz Kudelko1, Rakesh Sharma4, Wilson CW Chan1,2,5, Christopher M Overall3, Lisbet Haglund6, Pak C Sham7, Kathryn Song Eng Cheah1, Danny Chan1,2*

1School of Biomedical Sciences, , The University of Hong Kong, Hong Kong; 2The University of Hong Kong Shenzhen of Research Institute and Innovation (HKU-SIRI), Shenzhen, China; 3Centre for Blood Research, Faculty of Dentistry, University of British Columbia, Vancouver, Canada; 4Proteomics and Metabolomics Core Facility, The University of Hong Kong, Hong Kong; 5Department of Orthopaedics Surgery and Traumatology, HKU-Shenzhen Hospital, Shenzhen, China; 6Department of Surgery, McGill University, Montreal, Canada; 7Centre for PanorOmic Sciences (CPOS), The University of Hong Kong, Hong Kong

Abstract The spatiotemporal proteome of the intervertebral disc (IVD) underpins its integrity *For correspondence: and function. We present DIPPER, a deep and comprehensive IVD proteomic resource comprising [email protected] 94 genome-wide profiles from 17 individuals. To begin with, protein modules defining key †These authors contributed directional trends spanning the lateral and anteroposterior axes were derived from high-resolution equally to this work spatial proteomes of intact young cadaveric lumbar IVDs. They revealed novel region-specific Present address: ‡Department profiles of regulatory activities and displayed potential paths of deconstruction in the level- and of Orthopaedics Surgery and location-matched aged cadaveric discs. Machine learning methods predicted a ‘hydration Traumatology, HKU-Shenzhen matrisome’ that connects extracellular matrix with MRI intensity. Importantly, the static proteome Hospital, Shenzhen, China; used as point-references can be integrated with dynamic proteome (SILAC/degradome) and §Triskelion BV, Zeist, Netherlands transcriptome data from multiple clinical samples, enhancing robustness and clinical relevance. The Competing interest: See data, findings, and methodology, available on a web interface (http://www.sbms.hku.hk/dclab/ page 31 DIPPER/), will be valuable references in the field of IVD biology and proteomic analytics. Funding: See page 31 Received: 16 November 2020 Accepted: 30 December 2020 Introduction Published: 31 December 2020 The 23 intervertebral discs (IVDs) in the human spine provide stability, mobility, and flexibility. IVD degeneration (IDD), most common in the lumbar region (Saleem et al., 2013; Teraguchi et al., Reviewing editor: Subburaman Mohan, Loma Linda University, 2014), is associated with a decline in function and a major cause of back pain, affecting up to 80% United States of the world’s population at some point in life (Rubin, 2007), presenting significant socioeconomic burdens. Multiple interacting factors such as genetics, influenced by ageing, mechanical and other Copyright Tam et al. This stress factors, contribute to the pathobiology, onset, severity, and progression of IDD (Munir et al., article is distributed under the 2018). terms of the Creative Commons Attribution License, which IVDs are large, avascular, extracellular matrix (ECM)-rich structures comprising three compart- permits unrestricted use and ments: a hydrated nucleus pulposus (NP) at the centre, surrounded by a tough annulus fibrosus (AF) redistribution provided that the at the periphery, and cartilaginous endplates of the adjoining vertebral bodies (Humzah and original author and source are Soames, 1988). The early adolescent NP is populated with vacuolated notochordal-like cells, which credited. are gradually replaced by small chondrocyte-like cells (Risbud et al., 2015). Blood vessels terminate

Tam, Chen, et al. eLife 2020;9:e64940. DOI: https://doi.org/10.7554/eLife.64940 1 of 37 Tools and resources Computational and Systems Biology

eLife digest The backbone of vertebrate animals consists of a series of bones called vertebrae that are joined together by disc-like structures that allow the back to move and distribute forces to protect it during daily activities. It is common for these intervertebral discs to degenerate with age, resulting in back pain and severely reducing quality of life. The mechanical features of intervertebral discs are the result of their proteins. These include extracellular matrix proteins, which form the external scaffolding that binds cells together in a tissue, and signaling proteins, which allow cells to communicate. However, how the levels of different proteins in each region of the disc vary with time has not been fully examined. To establish how protein composition changes with age, Tam, Chen et al. quantified the protein levels and gene activity (which leads to protein production) of intervertebral discs from young and old deceased individuals. They found that the position of different mixtures of proteins in the intervertebral disc changes with age, and that young people have high levels of extracellular matrix proteins and signaling proteins. Levels of these proteins decreased as people got older, as did the amount of proteins produced. To determine which region of the intervertebral disc different proteins were in, Tam, Chen et al. also performed magnetic resonance imaging (MRI) of the samples to correlate image intensity (which represents water content) with the corresponding protein signature. The data obtained provides a high-quality map of how the location of different proteins changes with age, and is available online under the name DIPPER. This database is an informative resource for research into skeletal biology, and it will likely advance the understanding of intervertebral disc degeneration in humans and animals, potentially leading to the development of new treatment strategies for this condition.

at the endplates, nourishing and oxygenating the NP via diffusion, whose limited capacity mean that NP cells are constantly subject to hypoxic, metabolic, and mechanical stresses (Urban et al., 2004). With ageing and degeneration, there is an overall decline in cell ‘health’ and numbers (Rodriguez et al., 2011; Sakai et al., 2012), disrupting homeostasis of the disc proteome. The ECM has key roles in biomechanical function and disc hydration. Indeed, a hallmark of IDD is reduced hydration in the NP, diminishing the disc’s capacity to dissipate mechanical loads. Clinically, T-2 weighted MRI is the gold standard for assessing IDD, that uses disc hydration and structural features such as bulging or annular tears to measure severity (Pfirrmann et al., 2001; Schneiderman et al., 1987). The hydration and mechanical properties of the IVD are dictated by the ECM composition, which is produced and maintained by the IVD cells. To meet specific biomechanical needs, cells in the IVD compartments synthesise different compositions of ECM proteins. Defined as the ‘matrisome’ (Naba et al., 2012), the ECM houses the cells and facilitates their inter-communication by regulation of the availability and presentation of signalling molecules (Taha and Naba, 2019). With ageing or degeneration, the NP becomes more fibrotic and less compliant (Yee et al., 2016), ultimately affecting disc biomechanics (Newell et al., 2017). Changes in matrix stiffness can have a profound impact on cell-matrix interactions and downstream transcriptional regulation, signalling activity, and cell fate (Park et al., 2011). The resulting alterations in the matrisome lead to vicious feedback cycles that reinforce cellular degeneration and ECM changes. Notably, many of the associated IDD genetic risk factors, such as COL9A1 (Jim et al., 2005), ASPN (Song et al., 2008), and CHST3 (Song et al., 2013), are variants in genes encoding matrisome proteins, highlighting their importance for disc function. Therefore, knowledge of the cellular and extracellular proteome and their spatial distribution in the IVD is crucial to understanding the mechanisms underlying the onset and progression of IDD (Feng et al., 2006). Current knowledge of IVD biology is inferred from a limited number of transcriptomic studies on human (Minogue et al., 2010; Riester et al., 2018; Rutges et al., 2010) and animal (Veras et al., 2020) discs. Studies showed that cells in young healthy NP express markers including CD24, KRT8, KRT19, and T (Fujita et al., 2005; Minogue et al., 2010; Rutges et al., 2010), whereas NP cells in aged or degenerated discs have different and variable molecular signatures (Chen et al., 2006;

Tam, Chen, et al. eLife 2020;9:e64940. DOI: https://doi.org/10.7554/eLife.64940 2 of 37 Tools and resources Computational and Systems Biology Rodrigues-Pinto et al., 2016), such as genes involved in TGFb signalling (TGFA, INHA, INHBA, BMP2/6). The healthy AF expresses genes including collagens (COL1A1 and COL12A1) (van den Akker et al., 2017), growth factors (PDGFB, FGF9, VEGFC), and signalling molecules (NOTCH and WNT) (Riester et al., 2018). Although transcriptomic data provides valuable cellular information, it does not faithfully reflect the molecular composition. Cells represent only a small fraction of the disc volume, transcriptome-proteome discordance does not enable accurate predictions of protein levels from mRNA (Fortelny et al., 2017), and the disc matrisome accumulates and remodels over time. Proteomic studies on animal models of IDD, including murine (McCann et al., 2015), canine (Erwin et al., 2015), and bovine (Caldeira et al., 2017), have been reported. Nevertheless, human- animal differences in cellular phenotypes and mechanical loading physiologies mean that these findings might not translate to the human scenario. So far, human proteomic studies have compared IVDs with other cartilaginous tissues (O¨ nnerfjord et al., 2012) and have shown increases in fibrotic changes in aging and degeneration (Yee et al., 2016), a role for inflammation in degenerated discs (Rajasekaran et al., 2020), the presence of haemoglobins and immunoglobulins in discs with spon- dylolisthesis and herniation (Maseda et al., 2016), and changes in proteins related to cell adhesion and migration in IDD (Sarath Babu et al., 2016). The reported human disc proteomes were limited in the numbers of proteins identified and finer compartmentalisation within the IVD, and disc levels along the lumbar spine have yet to be studied. Nor have the proteome dynamics in term of ECM remodelling (synthesis and degradation) in young human IVDs and changes in ageing and degeneration been described. In this study, we presented DIPPER (the Big Dipper are point-reference stars for guiding nautical voyages), a comprehensive disc proteomic resource, comprising static spatial proteome, dynamic proteome and transcriptome, and a methodological flow, for studying the human IVD in youth, ageing, and degeneration. First, we established a high-resolution point-reference map of static spatial proteomes along the lateral and anteroposterior directions of IVDs at three lumbar levels, contributed by a young (16M) and an aged (59M) cadavers with no reported scoliosis or degeneration. We evaluated variations among the disc compartments and levels by principal component analysis (PCA), analysis of variance (ANOVA), and identification of differentially expressed proteins (DEPs). We discovered modules containing specific sets of proteins that describe the directional trends of a young IVD, and the deconstruction of these modules with ageing and degeneration. Using a LASSO regression model, we identified proteins (the hydration matrisome) predictive of tissue hydration as indicated by high-resolution MRI of the aged discs. Finally, we showed how the point-reference proteomes can be utilised, to integrate with other independent transcriptome and dynamic proteome (SILAC and degradome) datasets from 15 additional clinical disc specimens, elevating the robustness of the proteomic findings. An explorable web interface hosting the data and findings is presented, serving as a useful resource for the scientific community.

Results Disc samples and their phenotypes DIPPER comprises 94 genome-wide measurements from lumbar disc components of 17 individuals (Figure 1A; Table 1), with data types ranging from label-free proteomics, transcriptomics, SILAC to degradome (Lo´pez-Otı´n and Overall, 2002). High-resolution static spatial proteomes were generated from multiple intact disc segments of young trauma-induced (16 M) and aged (57 M) cadaveric spines. T1- and T2-weighted MRI (3T) showed the young discs (L3/4, L4/5, L5/S1) were non-degenerated, with a Schneiderman score of 1 from T2 images (Figure 1B). The NP of young IVD were well hydrated (white) with no disc bulging, endplate changes, or observable inter-level variations (Figure 1B; Figure 1—figure supplement 1A), consistent with healthy discs and were deemed fit to serve as a benchmarking point-reference. To investigate structural changes associated with ageing, high-resolution (7T) MRI was taken for the aged discs (Figure 1C). All discs had irregular endplates, and annular tears were present (green arrowheads) adjacent to the lower endplate and extending towards the posterior region at L3/4 and L4/5 (Figure 1C). The NP exhibited regional variations in hydration in both sagittal and transverse images (Figure 1C). Morphologically, the aged discs were less hydrated and the NP and AF structures less distinct, consistent with gross observations (Fig- ure 1—figure supplement 1A). Scoliosis was not detected in these two individuals.

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A DIPPER: 17 individuals and 94 genome-wide pro!les B C T11/T1 L3/4 L3/4 L3/4 High-resolution Newly static spatial synthesised Degradome Transcriptome T12/L1 proteome (SILAC) (n=6; N=12) (n=4; N=8) (n=2; N=66) (n=5; N=8) L1/2 P L4/5 L4/5 L4/5 Cross compare & validate L2/3 11 proteomic pro!les per disc L3/4 L4/5 L5/S1 L3/4 L4/5 L5/S1 L3/4 Young cadaver Aged cadaver L4/5 Raw data (n=66) L5/S1 L5/S1 L5/S1

PCA, SVM, Detection LASSO L5/S1 A A ANOVA of DEPs L R L R Regression P P & clustering Functional with analyses analyses MRI intensities T2 T1 H T2 T2 T2 Young Aged D A E IAF NP/IAF OAF 72 126 NP 102 14 9 Collagens Proteoglycans 158 14 VB Young and aged 1337 60 31.9 32 31.6 31.5 29.7 29.7 29.9 29.9 35.0 35.7 36.2 36.5 34.3 34.7 35.4 34.3 LR (in aggregate 3,100) 40 IVD 983 40 96 1 3751 35 VB P 44 LFQ) 2 35 IAF NP/IAF OAF NP 30 54 86 30 89 13 15 125 24 536 45 Young discs only 25 (in aggregate 1,883) 25 Anterior (A) 690 Intensities (log 11 pro!les 89 3 3538 20 corresponding to OAF 43 NP AF NP NP NP 11 locations IAF O IAF OAF IAF OAF IAF OAF NP/IAF IAF NP/IAF NP/IAF NP/IAF NP/IAF NP/IAF (L) (R) OAF NP OAF IAFNP/IAF NP NP/IAF IAF OAF 44 80 Left Right 117 9 7 Glycoproteins ECM affiliated 180 12 NP/IAF Aged discs only 1314 34 29.8 30.0 30.1 29.6 29.1 29.1 29.3 29.2 29.1 29.1 28.9 29.3 28.0 28.4 28.8 28.6 (in aggregate 2,791) OAF 803 40 36 109 1 Posterior (P) 2831 25 LFQ) 2 35 32

F 30 Age-group Core matrisome non-Core matrisome 28 Young Collagens ECM regulators 25 Aged Proteoglycans ECM a"liated 24 Intensities (log Glycoproteins Secreted factors

NP NP AF NP AF NP non-matrisome proteins IAF OAF IAF O IAF O IAF OAF NP/IAF NP/IAF NP/IAF NP/IAF ECM regulators Secreted factors

28.4 29.0 28.4 28.4 29.5 29.6 29.3 28.7 28.3 28.2 28.3 27.4 27.0 27.0 27.0 26.9 35 1000 1500

35 LFQ)

2 30

30 detected per sample

25 25 Absolute number of protein genes number of protein Absolute 0 500 Intensities (log 20 age-group 20

NP NP NP NP IAF OAF IAF OAF IAF OAF IAF OAF NP/IAF NP/IAF NP/IAF NP/IAF L3/4 NP L4/5 NP L3/4 NP L4/5 NP L5/S1 NP L5/S1 NP I L3/4 L IAF L4/5 L IAF L3/4 R IAF L4/5 R IAF L3/4 A OAF L4/5 A OAF L3/4 L OAF L4/5 L OAF L3/4 P OAF L4/5 P OAF L3/4 R OAF L4/5 R OAF L3/4 L IAF L4/5 L IAF L3/4 R IAF L4/5 R IAF L3/4 A OAF L4/5 A OAF L3/4 L OAF L4/5 L OAF L3/4 P OAF L4/5 P OAF L3/4 R OAF L4/5 R OAF L5/S1 L IAF L5/S1 R IAF L5/S1 A OAF L5/S1 L OAF L5/S1 P OAF L5/S1 R OAF L5/S1 L IAF L5/S1 R IAF L5/S1 A OAF L5/S1 L OAF L5/S1 P OAF L5/S1 R OAF L3/4 A NP/IAF L4/5 A NP/IAF L3/4 L NP/IAF L4/5 L NP/IAF L3/4 P NP/IAF L4/5 P NP/IAF L3/4 R NP/IAF L4/5 R NP/IAF L3/4 A NP/IAF L4/5 A NP/IAF L3/4 L NP/IAF L4/5 L NP/IAF L3/4 P NP/IAF L4/5 P NP/IAF L3/4 R NP/IAF L4/5 R NP/IAF

L5/S1 A NP/IAF L5/S1 L NP/IAF L5/S1 P NP/IAF L5/S1 R NP/IAF L5/S1 A NP/IAF L5/S1 L NP/IAF L5/S1 P NP/IAF L5/S1 R NP/IAF 60 G 40 20 counts Protein 0

Secreted rial

20 ulin ases factors (N=83) amily r 0 ATPases ydrolases K Zinc fingers ECM Proteasome PDZ domain 0LWRFKRQG ,íVHWGRPDLQ CD molecules 3KRVSKDWDVHV a"liated (N=47) Immunoglob 0 15 HKHOLFDOGRPDLQ ()íKDQGGRPDLQ RNA binding motif Ribosomal proteins Major spliceosome WD repeat domain +HDWVKRFNSURWHLQV L ribosomal proteins ComplementS ribosomal system proteins regulatory subunits GlycosyltransfeGlycoside Intermediate filaments Protein3URWHLQSKRVSKDWDVH coat complexes XFOHRODU51$KRVWJHQHV Spliceosomal B complex SpliceosomalSpliceosomalSpliceosomal A complex C complex P complex

ECM Armadillo lik 25 regulators Small n Ras small GTPases superf (N=109) 0 r=0.785 JK -15 29.4729.7 30.0231.5628.1 28.9229.9530.47 (p=6.5×10 ) 34 Glyco- 50 proteins OAF (N=114) 32 IAF NP/IAF

0 NP Proteo- 30 15 (log#LFQ)

glycans (N=23) 0 expression 28 Collagens (N=30) 0 15 (log#LFQ) 26 age-group young

24 histones

27 28 29 30 31 32 aged KLVWRQHV

30 31 32 33 34 NP NP IAF OAF IAF OAF L3/4 NP L4/5 NP L3/4 NP L4/5 NP GAPDH (log#LFQ)

L5/S1 NP L5/S1 NP NP/IAF NP/IAF

L3/4 L IAF L4/5 L IAF L3/4 R IAF L4/5 R IAF L3/4 A OAF L4/5 A OAF L3/4 L OAF L4/5 L OAF L3/4 P OAF L4/5 P OAF L3/4 R OAF L4/5 R OAF L3/4 L IAF L4/5 L IAF L3/4 R IAF L4/5 R IAF L3/4 A OAF L4/5 A OAF L3/4 L OAF L4/5 L OAF L3/4 P OAF L4/5 P OAF L3/4 R OAF L4/5 R OAF Young Aged L5/S1 L IAF L5/S1 R IAF L5/S1 A OAF L5/S1 L OAF L5/S1 P OAF L5/S1 R OAF L5/S1 L IAF L5/S1 R IAF L5/S1 A OAF L5/S1 L OAF L5/S1 P OAF L5/S1 R OAF L3/4 A NP/IAF L4/5 A NP/IAF L3/4 L NP/IAF L4/5 L NP/IAF L3/4 P NP/IAF L4/5 P NP/IAF L3/4 R NP/IAF L4/5 R NP/IAF L3/4 A NP/IAF L4/5 A NP/IAF L3/4 L NP/IAF L4/5 L NP/IAF L3/4 P NP/IAF L4/5 P NP/IAF L3/4 R NP/IAF L4/5 R NP/IAF L5/S1 A NP/IAF L5/S1 L NP/IAF L5/S1 P NP/IAF L5/S1 R NP/IAF L5/S1 A NP/IAF L5/S1 L NP/IAF L5/S1 P NP/IAF L5/S1 R NP/IAF

Figure 1. Outline of samples, workflow, MRI, and global overview of data in DIPPER. (A) Schematic diagram showing the structure of the samples, data types, and flow of analyses in DIPPER. n is the number of individuals. N is the number of genome-wide profiles. (B) Clinical T2-weighted MRI images (3T) of the young lumbar discs in the sagittal and transverse plane (left and right panels), T1 MRI image of the young lumbar spine (middle panel). (C) High-resolution (7T) T2-weighted MRI of the aged lower lumbar spine in sagittal (left panel) and transverse plane (right panel). (D) Diagram showing the Figure 1 continued on next page

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Figure 1 continued anatomy of the IVD and locations where the samples were taken. VB: vertebral body; NP, nucleus pulposus; AF, annulus fibrosus; IAF: inner AF; OAF: outer AF; NP/IAF: a transition zone between NP and IAF. (E) Venn diagrams showing the overlaps of detected proteins in the four major compartments. Top panel, young and aged profiles; middle, young only; bottom, aged only. (F) Barchart showing the numbers of proteins detected per sample, categorised into matrisome (coloured) or non-matrisome proteins (grey). (G) Barcharts showing the composition of the matrisome and matrisome- associated proteins. Heights of bars indicate the number of proteins in each category expressed per sample. The N number in brackets indicate the aggregate number of proteins. (H) Violin plots showing the level of subcategories of ECMs in different compartments of the disc. The green number on top of each violin shows its median. LFQ: label-free quantification. (I) Top 30 HGNC gene families for all non-matrisome proteins detected in the dataset. (J) Violin plots showing the averaged expression levels of 10 detected histones across the disc compartments and age-groups. (K) Scatter-plot showing the co-linearity between GAPDH and histones. The online version of this article includes the following figure supplement(s) for figure 1: Figure supplement 1. Gross images of the discs and overview of the static spatial proteomic data. Figure supplement 2. Numbers of proteins detected and protein levels for each combination of sample groups. Figure supplement 3. Heatmaps showing different categories of detected proteins. Figure supplement 4. Histones and housekeeping genes reflect cellularities.

Information of the disc samples used in the generation of other profiling data are described in Materials and methods and Table 1. They are clinical samples taken from patients undergoing surgery. The disc levels and intactness varied, and thus are more suitable for cross-validation purposes and some are directly relevant to IDD.

Quality data detects large numbers of matrisome and non-matrisome proteins The intact discs from the two cadaveric spines enabled us to derive spatial proteomes for young and aged human IVDs. We subdivided each lumbar disc into 11 key regions (Figure 1D), spanning the

Table 1. Summary of disc samples in DIPPER. Samples Age Sex Disc level/s Disc regions Reason for surgery Cadaver samples Young spine 16 M L3/4, L4/5, L5/S1 NP, NP/IAF, IAF, OAF N/A Aged spine 59 M L3/4, L4/5, L5/S1 NP, NP/IAF, IAF, OAF N/A Transcriptome samples YND74 17 M L1/2 NP, OAF Scoliosis YND88 16 M L1/2 NP, OAF Scoliosis AGD40 62 F L4/5 NP, OAF Degeneration AGD45 47 M L4/5 NP, OAF Degeneration SILAC samples YND148 19 F L2/3 OAF Scoliosis YND149 15 F L1/2 OAF Scoliosis YND151 15 F L1/2 NP, OAF Scoliosis YND152 14 F L1/2 NP, OAF Scoliosis AGD80 63 M L4/5 NP, OAF Degeneration Degradome samples YND136 17 F L1/2 NP, OAF Scoliosis YND141 20 F L1/2 NP, OAF Scoliosis AGD143 53 M L1/2 NP, OAF Trauma AGD62 55 F L5/S1 NP, OAF Degeneration AGD65 68 F L4/5 NP, OAF Degeneration AGD67 55 M L4/5 NP, OAF Degeneration

Tam, Chen, et al. eLife 2020;9:e64940. DOI: https://doi.org/10.7554/eLife.64940 5 of 37 Tools and resources Computational and Systems Biology outer-most (outer AF; OAF) to the central-most (NP) region of the disc, traversing both anteroposterior and lateral axes, adding valuable spatial information to our proteomic dataset. Since the disc is an oval shape, an inner AF (IAF) region was assigned in the lateral directions. A ‘mixed’ compartment between the NP and IAF with undefined boundary was designated as the NP/IAF in all four (anteroposterior and lateral) directions. In all, this amounted to 66 specimens with different compartments, ages, directions and levels, which then underwent LC-MS/MS profiling (Supplementary file 1). Systematic analyses of the 66 profiles are depicted in a flowchart (Figure 1A). A median of 654 proteins per profile were identified for the young samples and 829 proteins for the aged samples, with a median of 742 proteins per profile for young and aged combined. The proteome-wide distributions were on similar scales across the profiles (Figure 1—figure supplement 1B). Of the 3100 proteins detected in total, 418 were matrisome proteins (40.7% of all known matrisome proteins) and 2682 non-matrisome proteins (~14% of genome-wide non-matrisome genes) (Figure 1E; Figure 1—figure supplement 1C), and 983 were common to all four major compartments, namely the OAF, IAF, NP/IAF, and NP (Figure 1E, upper panel). A total of 1883 proteins were identified in young discs, of which 690 (36%) were common to all regions. Additionally, 45 proteins (2.4%) were unique to NP, whilst NP/IAF, IAF, and OAF had 86 (4.6%), 54 (2.9%), and 536 (28%) unique proteins, respectively (Figure 1E, middle panel). For the aged discs, 2791 proteins were identified, of which 803 (28.8%) were common to all regions. NP, NP/IAF, and IAF had 34 (12%), 80 (28.7%), and 44 (15%) unique proteins, respectively, with the OAF accounting for the highest proportion of 1314 unique proteins (47%) (Figure 1E, lower panel). The aged OAF had the highest number of detected proteins with an average of 1156, followed by the young OAF with an average of 818 (Figure 1F). The quantity and spectrum of protein categories identified suggest sufficient proteins had been extracted and the data are of high quality.

Levels of matrisome proteins decline in all compartments of aged discs We divided the detected matrisome proteins into core matrisome (ECM proteins, encompassing collagens, proteoglycans, and glycoproteins) and non-core matrisome (ECM regulators, ECM affiliated, and secreted factors), according to a matrisome classification database (Naba et al., 2012) (matrisomeproject.mit.edu)(Figure 1F). Despite the large range of total numbers of proteins detected (419 to 1920) across the 66 profiles (Figure 1F), all six subcategories of the matrisome contained similar numbers of ECM proteins (Figure 1F and G; Figure 1—figure supplement 2A). The non-core matrisome proteins were significantly more abundant in aged than in young discs (Fig- ure 1—figure supplement 2A). On average, 19 collagens, 18 proteoglycans, 68 glycoproteins, 52 ECM regulators, 22 ECM affiliated proteins, and 29 secreted factors of ECM were detected per profile. The majority of the proteins in these matrisome categories were detected in all disc compartments, and in both age groups. A summary of all the comparisons are presented in Figure 1— figure supplement 1C–E, and the commonly expressed matrisome proteins are listed in Table 2. Even though there are approximately three times more non-matrisome than matrisome proteins per profile on average (Figure 1F), their expression levels in terms of label-free quantification (LFQ) values are markedly lower (Figure 1—figure supplement 2B). Specifically, the expression levels of

core-matrisome were the highest, with an average log2(LFQ) of 30.65, followed by non-core matrisome at 28.56, and then non-matrisome at 27.28 (Figure 1—figure supplement 2B). Within the À core-matrisome, the expression was higher (p=6.4 Â 10 21) in young (median 30.74) than aged (median 29.72) discs (Figure 1—figure supplement 2C & D). This difference between young and aged discs is consistent within the subcategories of core and non-core matrisome, with the exception of the ECM regulator category (Figure 1H). The non-core matrisome and non-matrisome, however, exhibited smaller cross-compartment and cross-age differences in terms of expression levels (Figure 1—figure supplement 2E–H). That is, the levels of ECM proteins in each compartment of the disc declines with ageing and possibly changes in the relative composition, while the numbers of proteins detected per matrisome subcategory remain similar. This agrees with the concept that with ageing, ECM synthesis is not sufficient to counterbalance degradation, as exemplified in a proteoglycan study (Silagi et al., 2018).

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Table 2. Commonly expressed extracellular matrix (ECM) and associated proteins across all 66 profiles in the spatial proteome. Categories (Number of proteins) Protein names Core matrisome Collagens (13) COL1A1/2, COL2A1, COL3A1, COL5A1, COL6A1/2/3, COL11A1/2, COL12A1, COL14A1, COL15A1 Proteoglycans (14) ACAN, ASPN, BGN, CHAD, DCN, FMOD, HAPLN1, HSPG2, LUM, OGN, OMD, PRELP, PRG4, VCAN Glycoproteins (34) ABI3BP, AEBP1, CILP, CILP2, COMP, DPT, ECM2, EDIL3, EFEMP2, EMILIN1, FBN1, FGA, FGB, FN1, FNDC1, LTBP2, MATN2/3, MFGE8, MXRA5, NID2, PCOLCE, PCOLCE2, PXDN, SMOC1/2, SPARC, SRPX2, TGFBI, THBS1/2/4, TNC, TNXB Other matrisome ECM affiliated proteins ANXA1/2/4/5/6, CLEC11A, CLEC3A/B, CSPG4, SEMA3C (10) ECM regulators (16) A2M, CD109, CST3, F13A1, HTRA1, HTRA3, ITIH5, LOXL2/3, PLOD1, SERPINA1/3/5, SERPINE2, SERPING1, TIMP1 Secreted factors (2) ANGPTL2, FGFBP2

Cellular activities inferred from non-matrisome proteins Although 86.5% (2682) of the detected proteins were non-matrisome, their expression levels were considerably lower than matrisome proteins across all sample profiles (Figure 1F). A functional categorisation according to the Human Genome Nomenclature Committee gene family annotations (Yates et al., 2017) showed many categories containing information for cellular components and activities, with the top 30 listed in Figure 1I. These included transcriptional and translational machin- eries, post-translational modifications, mitochondrial function, protein turnover and importantly, transcriptional factors, cell surface markers, and inflammatory proteins that can inform gene regulation, cell identity, and response in the context of IVD homeostasis, ageing, and degeneration. These functional overviews highlighted 77 DNA-binding proteins and/or transcription factors, 83 cell surface markers, and 175 inflammatory-related proteins, with their clustering data presented as heatmaps (Figure 1—figure supplement 3). Transcription factors and cell surface markers are detected in some profiles (Figure 1—figure supplement 3A and B). The heatmap of the inflammatory-related proteins showed that more than half of the proteins are detected in the majority of samples, with four major clusters distinguished by age and expression levels (Figure 1—figure supplement 3C). For example, one of the clusters in the aged samples showed enrichment for com- À plement and coagulation cascades (False Discovery Rate, FDR q = 1.62 Â 10 21) and clotting factors À (FDR q = 6.05 Â 10 9), indicating potential infiltration of blood vessels. Lastly, there are 371 proteins involved in signalling pathways, and their detection frequency in the different compartments and heat map expression levels are illustrated in Figure 1—figure supplement 3D.

Histones and housekeeping genes inform compartment- and age- specific variations in cellularity Cellularity within the IVD, especially the NP, decreases with age and degeneration (Rodriguez et al., 2011; Sakai et al., 2012). We assessed whether cellularity of the different compartments could be inferred from the proteomic data. Quantitation of histones can reflect the relative cellular content of tissues (Wis´niewski et al., 2014). We detected 10 histones, including subunits of histone 1 (HIST1H1B/C/D/E, HIST1H2BL, HIST1H3A, HIST1H4A) and histone 2 (HIST2H2AC, HIST2H2BE, HIS- T2H3A), with four subunits identified in over 60 sample profiles that are mutually co-expressed (Fig- ure 1—figure supplement 4A). Interestingly, histone concentrations, and thus cellularity, increased from the inner to the outer compartments of the disc, and showed a highly significant decrease in À aged discs compared to young discs across all compartments (Figure 1J, Wilcoxon p=5.6 Â 10 4 ; Figure 1—figure supplement 4B). GAPDH and ACTA2 are two commonly used reference proteins, involved in the metabolic process and the cytoskeletal organisation of cells, respectively. They are expected to be relatively constant between cells and are used to quantify the relative cellular content of tissues (Barber et al., 2005). They were detected in all 66 profiles. GAPDH and ACTA2 amounts were significantly corre- À lated with a Pearson correlation coefficient (PCC) of 0.794 (p=9.3 Â 10 9)(Figure 1—figure supplement 4C), and they were both significantly co-expressed with the detected histone subunits, with

Tam, Chen, et al. eLife 2020;9:e64940. DOI: https://doi.org/10.7554/eLife.64940 7 of 37 Tools and resources Computational and Systems Biology PCCs of 0.785 and 0.636, respectively (Figure 1K; Figure 1—figure supplement 4D). As expected, expression of the histones, GAPDH, and ACTA2 was not correlated with two core-matrisome proteins, ACAN and COL2A1 (Figure 1—figure supplement 4E), whereas ACAN and COL2A1 were significantly co-expressed (Figure 1—figure supplement 4F), as expected due to their related regulation of expression and tissue function. Thus, cellularity information can be obtained from proteomic information, and the histone quantification showing reduced cellularity in the aged IVD is consistent with the reported changes (Rodriguez et al., 2011; Sakai et al., 2012).

PCA captures the information content distinguishing age and tissue types To gain a global overview of the data, we performed PCA on a set of 507 proteins selected compu- tationally, allowing maximal capture of valid values, while incurring minimal missing values, followed by imputations (Figure 2—figure supplement 1; Materials and methods). The first two principal components (PCs) explained a combined 65.5% of the variance, with 39.9% and 25.6% for the first and second PCs, respectively (Figure 2A and B). A support vector machine with polynomial kernel was trained to predict the boundaries: it showed PC1 to be most informative to predict age, with a clear demarcation between the two age groups (Figure 2A, vertical boundary), whereas PC2 distinguished disc sample localities, separating the inner compartments (NP, NP/IAF, and IAF) from the OAF (Figure 2A, horizontal boundary). PC3 captured only 5.0% of the variance (Figure 2C & D), but it distinguished disc level, separating the lowest level (L5/S1) from the rest of the lumbar discs (L3/4 and L4/5) (Figure 2D, horizontal boundary). Samples in the upper level (L3/4 and L4/5) appeared to be more divergent, with the aged disc samples deviating from the young ones (Figure 2D).

Top correlated genes with the PCs are insightful of disc homeostasis To extract the most informative features of the PCA, we performed proteome-wide associations with each of the top three PCs, which accounted for over 70% of total variance, and presented the top 100 most positively and top 100 most negatively correlated proteins for each of the PCs (Figure 2E–G). As expected, the correlation coefficients in absolute values were in the order of PC1 > PC2 > PC3 (Figure 2E–G). The protein content is presented as non-matrisome proteins (grey colour) and matrisome proteins (coloured) that are subcategorised as previously. For the negatively correlated proteins, the matrisome proteins contributed to PC1 in distinguishing young disc samples, as well as to PC2 for sample location within the disc, but less so for disc level in PC3. Further, the relative composition of the core and non-core matrisome proteins varied between the three PCs, depict- ing the dynamic ECM requirement and its relevance in aging (PC1), tissue composition within the disc (PC2) and mechanical loading (PC3). PC1 of young discs identified known chondrocyte markers, CLEC3A (Lau et al., 2018) and LECT1/2 (Zhu et al., 2019), hedgehog signalling proteins, HHIPL2, HHIP, and SCUBE1 (Johnson et al., 2012), and xylosyltransferase-1 (XYLT1), a key enzyme for initiating the attachment of glycosaminoglycan side chains to proteoglycan core proteins (Silagi et al., 2018; Figure 2E). Most of the proteins that were positively correlated in PC1 were coagulation factors or coagulation related, suggesting enhanced blood infiltration in aged discs. PC2 implicated key changes in molecular signalling proteins (hedgehog, WNT and Nodal) in the differences between the inner and outer disc regions (Figure 2F). Notably, PC2 contains heat shock proteins (HSPA1B, HSPA8, HSP90AA1, HSPB1) which are more strongly expressed in the OAF than in inner disc, indicating the OAF is under stress (Takao and Iwaki, 2002). Although the correlations in PC3 were much weaker, proteins such as CILP/CILP2, DCN, and LUM were associated with lower disc level.

ANOVA reveals the principal phenotypes for explaining variability in categories of ECMs To investigate how age, disc compartment, level, and direction affect the protein profiles, we carried out ANOVA for each of these phenotypic factors for the categories of matrisome and non-matrisome proteins (Figure 2—figure supplement 1H–J). In the young discs, the dominant phenotype explaining the variances for all protein categories was disc compartment. It is crucial that each disc compartment (NP, IAF, and OAF) has the appropriate protein composition to function correctly (Fig- ure 2—figure supplement 1I). This also fits the understanding that young healthy discs are axially

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AEFGTop correlated with PC1 Top correlated with PC2 Top correlated with PC3 young í 0.0 0.5 í 0.0 0.5 í 0.0 0.5 aged NP NP/IAF COL11A2 COL9A1 COL1A1 COL2A1 COL5A1 3 COL16A1 2 COL5A2 COL11A1 OMD IAF COL3A1 BCAN DCN COL1A2 CHADL 3 LUM COL11A1 EPYC PRELP OAF COL15A1 13 EDIL3 OGN 8 COL5A1 MXRA5 BGN COL6A2 IGFBP5 HAPLN1 COL9A1 EMILIN1 KERA COL6A3 CRISPLD1 COMP COL6A1 PCOLCE2 LTBP2 COL8A1 SMOC2 CILP HSPG2 SLIT3 14 SRPX2 VCAN SRPX CILP2 ACAN AEBP1 THBS1 11 HAPLN3 SMOC1 EFEMP2 CHAD 8 IGFBP7 THBS2 HAPLN1 VIT IGFBP6 BGN MATN3 LAMA4 0.5 ASPN CSPG4 LAMB2 FNDC1 SEMA3C CLEC11A

PC2 (25.6%) 3 THBS3 SEMA3A LMAN1 3 ABI3BP SERPINE2 ANXA5 0 PCOLCE SERPINA5 MMP3 í FBN1 PAMR1 CTSD EMILIN3 SERPING1 CTSF TNXB TIMP2 F13A1 6 MATN2 PLOD1 CTSB MATN3 CST3 13 LOX EMILIN1 19 PLOD2 GDF5 IGFBP7 CD109 SFRP4

í ECM2 TIMP1 ANGPTL7 5 SPP1 ITIH5 S100A4 0 SLIT3 SLPI S100A9 VIT LOXL2 WISP2

0.5 −20 0 20 40 60 LTBP4 INHBA H1FX LAMA5 SCUBE3 FTH1 NID2 CCL18 CLIC4 −20 −10 0 10 20 30 40 TNC TGFB1 TALDO1 CLEC3A S100A6 PDIA3 ANXA1 CHRD PPP2R1A ANXA4 IL17B GBA PC1 (39.9%) LGALS3 6 CXCL12 NOV B ANXA2 ANGPTL4 17 ENO2 GPC1 PDGFC CHID1 CD109 SCUBE1 HSP90B1 young LOX FSTL1 HIST1H2BL L3/4 SLPI CCL14 DPP7 ITIH5 6 FRZB SIL1 aged LOXL2 CHRDL2 CA1 L4/5 SERPINA5 FGFBP2 RPL15 IL17B MDK SOST FRZB C1S GAA L5/S1 HHIP APOD UTRN SCUBE1 QSOX1 GANAB CCL18 8 ISLR HEXB ANGPTL7 FAP ARF4 CHRDL2 XYLT1 ACTR3 S100A1 CLSTN1 HSP90AB1 CALU MFI2 MAMDC2 HHIPL2 TNFRSF11B HBG1 CKB DKK3 AOC2 PGAM1 ATRN CHD9 LECT2 SCRG1 RCN3 TPI1 MINPP1 GSTP1 SSC5D CHST3 CLIC1 CKM PDGFRL ATP6AP2 65 RCN3 HEXA PNP LECT1 CFHR2 ARF1 PYGB HHIPL1 NNMT RNASE4 CPQ CA2 LYZ GALNT2 AKR1A1

PC2 (25.6%) ANG KRT8 SKP1 ENPP2 GGH RYR1 RNASE3 GRN CFD DSC3 CRTAC1 47 ARHGDIA VIM MSMP ITGBL1 CAPS KAL1 CAPG LPL PPIC PGM2 XYLT1 40 CYTL1 LDHA PRDX1 LPL Collagens PPIA ALDOA OAF YWHAZ UGP2 ACLY Proteoglycans MYOC ITGB1 PXYLP1 NT5E MYH1 CCDC80 PDIA6 KRT14 CP Glycoproteins CLU MFI2 DNAJC3 TKT −20 0 20 40 60 PYGM POFUT2 ECM a!liated GSTO1 CRYAB RNASE4 CALR RCN1 APMAP P4HB −20 −10 0 10 20 30 40 LDHA VASN ECM regulators KRT10 VASN APOM KRT1 ACTA2 PYGL Secreted factors HSP90AA1 PC1 (39.9%) GAPDH KRT19 HSPA5 PGK1 C1R HIST1H4A C CD9 DDX39B Others CFL1 GRN NUCB1 RPS3 KRT8 CHI3L1 NCL MDH1 CHPF TLN1 LRG1 1 COL4A2 IGFALS young VTN LAMB1 MFAP4 FGA FBLN1 IGFBP3 7 FGB 7 LAMB2 7 EFEMP1 aged FGG NID1 SLIT3 ECM1 LAMC1 CRISPLD1 IGFALS SERPINH1 LRG1 1 HPX 2 SERPINB1 1 CLEC3B HTRA1 CAPZB SERPIND1 F13A1 HSPA1B SERPINF2 MMP2 LMNA SERPINA4 SERPINA1 PDIA6 SERPINA3 F9 ILF3 SERPINA1 A2M HIST1H2BL SERPINA6 F13B YWHAZ 13 AGT HABP2 RPS8 F13B F10 GPI SERPING1 ITIH1 +/$í$ SERPINA7 SERPINA7 GLUD1 F12 SERPINA6 CAPG HABP2 24 F12 HNRNPK KNG1 SERPINA10 ARPC2 FSTL1 ITIH2 SYNCRIP 2 ANGPTL4 ITIH4 TPM2 CFHR5 PLG SSB DBNL F2 HSPA8 HSPA2

PC3 (5.0%) KNG1 SET CD163 SERPINC1 RNH1 BIN1 SERPIND1 HBA1 BTD SERPINF2 PRDX2 IGHG3 HRG CKAP4 UGP2 NP SERPINA4 IDH2 VDAC1 CRLF1 EEF2 PROS1 3 ANGPTL4 CFL1 PRSS3P2 NP/IAF MST1 GDI2 PXYLP1 C4B ESD IGJ HOXD1 ACO2 CKM IAF APOL1 HNRNPA2B1 KAL1 GPLD1 VCL C2 CRP EIF4A1 IGHD OAF CFH CALR GPLD1 ,*.9í HSP90AA1 CCT3 −15 −10 −5 0 5 10 15 20 C2 HDGF A1BG CD5L HBB ENO3 APOB GSTP1 CFHR2 −20 −10 0 10 20 30 40 BTD ATP5A1 ,*+9í IGJ MYL6B KRT19 APOC1 UBA1 LPL PC1 (39.9%) CFHR1 BLVRB ATRN D CFHR2 PLEC PLTP LGALS3BP CA2 APOL1 ORM1 SND1 IGLC6 ORM2 AHNAK TNNT3 young KLKB1 ATP5B GC APOM 92 CA1 C8A aged CD14 EIF5A CFH AZGP1 NCL ORM2 SHBG SELENBP1 MYOM2 APOE AKR1B1 KRT8 C5 HIST2H2AC RPS16 APCS CBR1 C8G IGHG4 FSCN1 77 HADHB HPR PFN1 DKK3 AFM YWHAQ C4B C3 HSPB1 TPM1 C4BPA TUBB CD5L IGLC6 HNRNPM ACTN2 65 ALB MYL6 TTN HP MAMDC2 APCS GC AKR1A1 TNNC2 APOC3 ACTG1 CFI AHSG RPL12 APOM IGKC HNRNPC ,*.9í APOA1 DPYSL3 CFB IGHG3 DDX17 MYBPC1 C6 ADH1B DES 0.5 P0DOX2 RPS3 LMNB1 0 IGHA1 RPL18 AHSG

PC3 (5.0%) í TF TPM3 MYH7 IGHM HNRNPD MYLPF IGLL5 MYH9 ACTN3 C7 OLFML1 PYGM A1BG RAP1B HPR C8B CYB5R3 IGHM IGK PCBP1 MYL3 LBP HNRNPA3 LDB3 L3/4 CFI TPM4 MYH1 APOA2 HNRNPU CD14 IGHG1 VCP SHBG L4/5 CPB2 CLIC1 ,*+9í APOH RPL3 TNNC1 L5/S1 IGHG2 CAP1 LBP PON1 HNRNPR MYL1 C8G SEPT7 APOA2 −15 −10 −5 0 5 10 15 20 APOA4 TAGLN2 MYL2 C9 CAPZA2 CP −20 −10 0 10 20 30 40 C8A HNRNPA1 IGHG4 IGHD PLS3 MYH2 PGLYRP2 ST13 P0DOX2 PC1 (39.9%) TTR MYL12A CASQ1

Figure 2. Principle component analysis (PCA) of the 66 static spatial profiles based on a set of 507 genes selected by optimal cutoff (see Figure 2— figure supplement 1A–C). (A) Scatter-plot of PC1 and PC2 colour-coded by compartments, and dot-shaped by age-groups. Solid curves are the support vector machines (SVMs) decision boundaries between inner disc regions (NP, NP/IAF, IAF) and OAF, and dashed curves are soft boundaries for probability equal to ±0.5 and are applied to all plots in this figure. (B) Scatter-plot of PC1 and PC2 colour-coded by disc levels. The SVM boundaries are Figure 2 continued on next page

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Figure 2 continued trained between L5/S1 and upper levels (L3/4 and L4/5). (C) Scatter-plot of PC1 and PC3, colour-coded by disc compartments. The SVM boundaries are trained between inner disc regions and OAF. (D) Scatter-plot of PC1 and PC3, colour-coded by disc levels. The SVM boundaries are trained between L5/S1 and upper levels (L3/4 and L4/5). (E) Top 100 positively and negatively correlated genes with PC1, colour-coded by ECM categories. (F) Top 100 positively and negatively correlated genes with PC2, colour-coded by extracellular matrix (ECM) categories. (G) Top 100 positively and negatively correlated genes with PC3, colour-coded by ECM categories. The online version of this article includes the following figure supplement(s) for figure 2: Figure supplement 1. Selection of genes for performing principle component analysis (PCA) and results of ANOVA on assessing global data variability.

symmetric and do not vary across disc levels. In aged discs, compartment is still relevant for non- matrisome proteins and collagen, but disc level and directions become influential for other protein categories, which is consistent with variations in mechanical loading occurring in the discs with ageing and degeneration (Figure 2—figure supplement 1J). In the combined (young and aged) disc samples, age was the dominant phenotype across major matrisome categories, while compartment best explained the variance in non-matrisome, reflecting the expected changes in cellular (non-matrisome) and structural (matrisome) functions of the discs (Figure 2—figure supplement 1H). This guided us to analyse the young and aged profiles separately, before performing cross-age comparisons.

The high-resolution spatial proteome of young and healthy discs PCA of the 33 young profiles showed a distinctive separation of the OAF from the inner disc regions on PC1 (upper panel of Figure 3A). In PC2, the lower level L5/S1 could generally be distinguished from the upper lumbar levels (lower panel of Figure 3A). The detected proteome of the young discs (Figure 3B) accounts for 9.2% of the human proteome (or 1883 out of the 20,368 on UniProt). We performed multiple levels of pairwise comparisons (summarised in Figure 3—figure supplement 1A) to detect proteins associated with individual phenotypes, using three approaches (see Materials and methods): statistical tests; proteins detected in one group only; or proteins using a fold-change threshold. We detected a set of 671 DEPs (Supplementary file 2) (termed the ‘variable set’), containing both matrisome and non-matrisome proteins (Figure 3D), and visualised in a heatmap (Figure 3—figure supplement 2), with identification of four modules (Y1-Y4).

Protein modules show lateral and anteroposterior trends To investigate how the modules are associated with disc components, we compared their protein

expression profiles along the lateral and anteroposterior axes. The original log2(LFQ) values were transformed to z-scores to be on the same scale. Proteins of the respective modules were superim- posed on the same charts, disc levels combined or separated (Figure 3E–H; Figure 3—figure supplement 3). Module Y1 is functionally relevant to NP, containing previously reported NP and novel markers KRT19, CD109, KRT8 and CHRD (Anderson et al., 2002), CHRDL2, SCUBE1 (Johnson et al., 2012), and CLEC3B. Proteins levels in Y1 are lower on one side of the OAF, increases towards the central NP, before declining towards the opposing side, forming a concave pattern in both lateral and anteroposterior directions, with a shoulder drop occurring between IAF and OAF (Figure 3E). À Module Y2 was enriched in proteins for ECM organisation (FDR q = 8.8 Â 10 24); Y3 was enriched À for proteins involved in smooth muscle contraction processes (FDR q = 8.96 Â 10 19); and Y4 was À enriched for proteins of the innate immune system (FDR q = 2.93 Â 10 20). Interestingly, these modules all showed a tendency for convex patterns with upward expression toward the OAF regions, in both lateral and anteroposterior directions, in all levels, combined or separated (Figure 3F–H; Fig- ure 3—figure supplement 3B–D). The higher proportions of ECM, muscle contraction, and immune system proteins in the OAF are consistent with the contractile function of the AF (Nakai et al., 2016), and with the NP being avascular and ‘immune-privileged’ in a young homeostatic environment (Sun et al., 2020).

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A B D NP A P Variable protein set Constant protein set NP/IAF Human proteome 27 (4.0%) 11 (4.5%) L IAF L L OAF L (20,368 proteins) R R 44 (6.6%) 15 (6.1%) Detected L P proteome 16 (2.4%) 9 (3.7%) L R (1,883 proteins) A 53 (7.9%) 24 (9.8%) R P RA PC2 (11.7%) L A R 11 (1.6%) 7 (2.9%) P A R 506 (75.5%) 171 (69.8%)

í L Variable Constant 13 (1.9%) 8 (3.3%) R set set A 0.5 L P P R 0 −15 −10 −5 0 5 10 15 −30 −20 −10 0 10 20 Marginally PC1 (36.5%) detected Major proteins in the constant set: L3/4 Collagens: COL1A2,COL2A1,COL9A1,COL15A1 L4/5 L5/S1 C Proteoglycans: ACAN,CHAD,HAPLN1,HSPG2,OMD,VCAN 245 Glycoproteins: ABI3BP,AEBP1,CTGF,DPT,ECM2,EFEMP2,EMILIN3,FNDC1,LTBP2, NID2,PXDN,TNC, TNXB ECM a'liated: CLEC3A,CLEC11A 0.5 0 >16 PC2 (11.7%) í ECM regulators: A2M,HTRA1,HTRA3,LOXL2,LOXL3,SERPINA1,SERPINA3,SERPINE1 Secreted factors: ANGPTL2,FGFBP2,PTN Detected in

Number of Pro"les Non-matrisome: ACTA2,ANG,C1R,C3,C4B,CALU,CAPS,CFH,CKB,CLU,CRTAC1,DSTN, −15 −10 −5 0 5 10 15

0 5 10 15 20 25 30 EEF1A1, EHD2,ENO1,FBXO2,FHL1,FTH1,FTL,GANAB,GSN,HHIPL2, −30 −20 −10 0 10 20 PC1 (36.5%) 1 200 400 600 800 1000 1200 HSPA5, IGK,KRT1, KRT10,LDHA,LRP1,LYZ,NEB,P4HB,PDIA4,PEBP1, Number of proteins PGAM1,PGK1, PGM1,PPIB, PRDX1,PRDX6,PTRF,PYGB,RNH1, SOD3,SSC5D,TPI1, TTN,TUBB4B,UGP2,VIM

E Module Y1: NP markers F Module Y2: AF markers 2 I 2 1 1 Higher in OAF SERPINF1 0 COL6A2 0 Higher in inner disc COL6A3

Z-scores −1 −1 YWHAZ Z-scores (L3/4)

(all levels mixed) −2 −2 L_OAFL_IAFL_NP/IAFNP R_NP/IAFR_IAFR_OAF COL5A2 COL6A1 L_OAFL_IAFL_NP/IAFNP R_NP/IAFR_IAFR_OAF ANXA6 2 LAMC1 1 HSPA1B CD109 COL1A1 2 0 GSTP1 BGN 1 −1 CSPG4 ANXA4 THBS4 LAMB2

Z-scores (L4/5) AOC2 0 −2 COL5A1 ANXA5 PLS3 COL11A1 AHNAK L_OAFL_IAFL_NP/IAFNP R_NP/IAFR_IAFR_OAF SEMA3C OGN Z-scores ANXA2 LMNA −1 PRELP THBS1 YWHAE CAP1 (all levels mixed) PDGFRL MXRA5 CILP2 UBA1 ACTN1 −2 2 FMOD A_OAF A_NP/IAFNP P_NP/IAFP_OAF SERPINA5 VIT EDIL3 ANXA1 HIST1H2BL 1 KRT8 CXCL12 GAPDH LAMA4 PRDX2 MFGE8 CLSTN1 HIST1H4A YWHAQ 0 ACTG1 TNFRSF11B ISLR CALM3 PPIA −log$&(FDR q) C1S KAL1 IQGAP1 HNRNPA2B1 APOD SMOC1 DCN HSPA8 SND1 ATP5B HPX −1 INHBA SERPINE2 CCDC80 PDIA3 GNAI2 KRT19 TIMP1 ARF1 GDI2 THBS2 SOD1 NCL SERPINC1 Z-scores (L5/S1) IL17B FRZB EMILIN1 −2 CTSD CFL1 HSPB1 VCL ORM1 L_OAFL_IAFL_NP/IAFNP R_NP/IAFR_IAFR_OAF CLEC3B OAF HSP90B1 AZGP1 SERPINH1 MYH9 RNASE4 SRPX2 RPS8 ALB CP CFB PDIA6 HBB VCP IGHA1 SERPING1 PCOLCE2 FBN1 MSN VWA1 COL12A1 APOA1 PXYLP1 DKK3 LPL SMOC2 HSP90AA1 CLIC1 HIST2H2AC CHRD PCOLCE CPQ COMP ALDOA FGA Module Y3: smooth muscle QSOX1 LOX DPYSL3 FGB G MATN3 MFI2 ATRN SLPI IGFBP7 TUBA1B ACTN4 cell markers H Module Y4: immune & blood SLIT3 TGFBI HBA1 CALR ITIH2 GALNT2 COL11A2 CILP PKM 2 CHRDL2 PLOD1 RPS3 ARF4 COL14A1 FGG NUCB1 PFN1 ) COL3A1 RPS27A 1 1 L3/4 0 0

−1 0 2 4 6 8 Z-scores (L3/4) −1 Z-scores ( L_OAFL_IAFL_NP/IAFNP R_NP/IAFR_IAFR_OAF L_OAFL_IAFL_NP/IAFNP R_NP/IAFR_IAFR_OAF −6 −4 −2 0 2 4 6 log#(fold-change) 2 ) 1 J 1 L4/5 0 Higher in inner disc (non-OAF) Higher in OAF 0 COL3A1, COL5A1, COL5A2, −1 −1 COL12A1, COL14A1, COL1A1, COL6A1, Collagens COL11A1, COL11A2 COL6A2, COL6A3 Z-scores (L4/5) Z-scores ( −2 −2 Proteoglycans BGN, DCN, FMOD, OGN, PRELP L_OAFL_IAFL_NP/IAFNP R_NP/IAFR_IAFR_OAF L_OAFL_IAFL_NP/IAFNP R_NP/IAFR_IAFR_OAF EDIL3, EMILIN1, IGFBP7, MATN3, MXRA5, CILP, CILP2, COMP, FBN1, FGA, FGB, Glycoproteins FGG, LAMA4, LAMB2, LAMC1, MFGE8, SRPX2, PCOLCE, PCOLCE2, SLIT3, SMOC1, SMOC2, VIT TGFBI, THBS1, THBS2, THBS4, VWA1

) 2 2 ECM a'liated CLEC3B, CSPG4, SEMA3C ANXA1, ANXA2, ANXA4, ANXA5, ANXA6, HPX 1 L5/S1 1 CD109, PLOD1, SERPINA5, SERPINE2, CTSD, ITIH2, LOX, SERPINC1, SERPINF1, ECM regulators SERPING1, SLPI, TIMP1 SERPINH1 0 0 Secrected factors CHRD, CHRDL2, CXCL12, FRZB, IL17B, INHBA −1 −1 Z-scores ( Basement COL6A1, COL6A2, COL6A3, LAMA4, LAMB2, Z-scores (L5/S1) −2 membranes LAMC1 L_OAFL_IAFL_NP/IAFNP R_NP/IAFR_IAFR_OAF L_OAFL_IAFL_NP/IAFNP R_NP/IAFR_IAFR_OAF Surface markers CD109

Figure 3. Delineating the young non-degenerated cadaveric discs’ static spatial proteome. (A) Principal component analysis (PCA) plot of all 33 young profiles. Curves in the upper panel show the support vector machine (SVM) boundaries between the OAF and inner disc regions, those in the lower panel separate the L5/S1 disc from the upper disc levels. L, left; R, right; A, anterior; P, posterior. (B) A schematic illustrating the partitioning of the detected human disc proteome into variable and constant sets. (C) A histogram showing the distribution of non-DEPs in terms of their detected Figure 3 continued on next page

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Figure 3 continued frequencies in the young discs. Only 245 non-DEP proteins were detected in over 16 profiles, which is thus defined to be the constant set; while the remaining ~1,000 proteins were considered marginally detected. (D) Piecharts showing the extracellular matrix (ECM) compositions in the variable (left) and constant (right) sets. The constant set proteins that were detected in all 33 young profiles are listed at the bottom. (E) Normalised expression (Z- scores) of proteins in the young module Y1 (NP signature) laterally (top panel) and anteroposterially (bottom panel), for all three disc levels combined. The red curve is the Gaussian Process Estimation (GPE) trendline, and the blue curves are one standard deviation above or below the trendline. (F) Lateral trends of module Y2 (AF signature) for each of the three disc levels. (G) Lateral trends of module Y3 (Smooth muscle cell signature) for each of the three disc levels. (H) Lateral trends of module Y4 (Immune and blood) for each of the three disc levels. (I) Volcano plot of differentially expressed proteins (DEPs) between OAF and inner disc (an aggregate of NP, NP/IAF, IAF), with coloured dots representing DEPs. (J) A functional categorisation of the DEPs in (I). The online version of this article includes the following figure supplement(s) for figure 3: Figure supplement 1. Additional comparisons within the young samples. Figure supplement 2. Heatmap of differentially expressed proteins (DEPs) and protein modules. Figure supplement 3. Modular trends along the lateral or anteroposterior axes in the young non-degenerated discs.

Inner disc regions are characterised with NP markers The most distinctive pattern on the PCA is the separation of the OAF from the inner disc, with 99 proteins expressed higher in the OAF and 55 expressed higher in the inner disc (Figure 3I,J). Nota- bly, OAF and inner disc contained different types of ECM proteins. The inner disc regions were enriched in collagens (COL3A1, COL5A1, COL5A2, and COL11A2), matrillin (MATN3), and proteins associated with ECM synthesis (PCOLCE) and matrix remodelling (MXRA5). We also identified in the inner disc previously reported NP markers (KRT19, KRT8) (Risbud et al., 2015), in addition to inhibitors of WNT (FRZB, DKK3) and BMP (CHRD, CHRDL2) signalling (Figure 3I,J). Of note, FRZB and CHRDL2 were recently shown to have potential protective characteristics in osteoarthritis (Ji et al., 2019). The TGFb pathway appears to be suppressed in the inner disc where antagonist CD109 (Bizet et al., 2011; Li et al., 2016) is highly expressed, and the TGFb activity indicator TGFBI is expressed higher in the OAF than in the inner disc.

The OAF signature is enriched with proteins characteristic of tendon and ligament The OAF is enriched with various collagens (COL1A1, COL6A1/2/3, COL12A1 and COL14A1), basement membrane (BM) proteins (LAMA4, LAMB2 and LAMC1), small leucine-rich proteoglycans (SLRP) (BGN, DCN, FMOD, OGN, PRELP), and BM-anchoring protein (PRELP) (Figure 3I,J). Tendon- related markers such as thrombospondins (THBS1/2/4) (Subramanian and Schilling, 2014) and cartilage intermediate layer proteins (CILP/CILP2) are also expressed higher in the OAF. Tenomodulin (TNMD) was exclusively expressed in 9 of the 12 young OAF profiles, and not in any other compartments (Supplementary file 2). This fits a current understanding of the AF as a tendon/ligament-like structure (Nakamichi et al., 2018). In addition, the OAF was enriched in actin-myosin (Figure 3I), suggesting a role of contractile function in the OAF, and in heat-shock proteins (HSPA1B, HSPA8, HSPB1, HSP90B1, HSP90AA1), suggesting a stress response to fluctuating mechanical loads.

Spatial proteome enables clear distinction between IAF and OAF We sought to identify transitions in proteomic signatures between adjacent compartments. The NP and NP/IAF protein profiles were highly similar (Figure 3—figure supplement 1B). Likewise, NP/IAF and IAF showed few DEPs, except COMP which was expressed higher in IAF (Figure 3—figure supplement 1C). OAF and NP (Figure 3—figure supplement 1R) and OAF and NP/IAF (Figure 3—figure supplement 1O) showed overlapping DEPs, consistent with NP and NP/IAF having highly similar protein profiles, despite some differences in the anteroposterior direction (Figure 3—figure supplement 1P & Q). The clearest boundary within the IVD, between IAF and OAF, was marked by a set of DEPs, of which COL5A1, SERPINA5, MXRA5 were enriched in the IAF, whereas LAMB2, THBS1, CTSD typified the OAF (Figure 3—figure supplement 1D). These findings agreed with the modular patterns (Figure 3E–H).

Tam, Chen, et al. eLife 2020;9:e64940. DOI: https://doi.org/10.7554/eLife.64940 12 of 37 Tools and resources Computational and Systems Biology The constant set represents the baseline proteome among structures within the young disc Of the 1,880 proteins detected, 1,204 proteins were not found to vary with respect to the phenotypic factors. The majority of these proteins were detected in few profiles (Figure 3B) and were not used in the comparisons. We set a cutoff for a detection in >1/2 of the profiles to prioritise a set of 245 proteins, hereby referred to as the ‘constant set’ (Figure 3C). Both the variable and the constant sets contained high proportions of ECM proteins (Figure 3D). Amongst the proteins in the constant set that were detected in all 33 young profiles were known protein markers defining a young NP or disc, including COL2A1, ACAN, and A2M (Risbud et al., 2015). Other key proteins in the constant set included CHAD, HAPLN1, VCAN, HTRA1, CRTAC1, and CLU. Collectively, these proteins showed the common characteristics shared by compartments of young discs, and they, alongside the variable set, form the architectural landscape of the young disc.

Diverse changes in the spatial proteome with ageing PCA was used to identify compartmental, directional, and level patterns for the aged discs (Figure 4A). Albeit less clear than for the young discs (Figure 3A), the OAF could be distinguished from the inner disc regions on PC1, explaining 46.7% of the total variance (Figure 4A). PC2 showed a more distinct separation of signatures from lumbar disc levels L5/S1 to the upper disc levels (L4/L5 and L3/4), accounting for 21.8% of the total variance (Figure 4A).

Loss of the NP signature from inner disc regions As with the young discs, we performed a series of comparative analyses (Figure 4C; Figure 4—figure supplement 1A–H; Supplementary file 3). Detection of DEPs between the OAF and the inner disc (Figure 4B), showed that 100 proteins were expressed higher in the OAF, similar to young discs (Figure 3C). However, in the inner regions, only nine proteins were significantly expressed higher, in marked contrast to the situation in young discs. Fifty-five of the 100 DEPs in the OAF region over- lapped in the same region in the young discs, but only 3 of the 9 DEPs in the inner region were identified in the young disc; indicating changes in both regions but more dramatic in the inner region. This suggests that ageing and associated changes may have initiated at the centre of the disc. The typical NP markers (KRT8/19, CD109, CHRD, CHRDL2) were not detectable as DEPs in the aged disc; but CHI3L2, A2M and SERPING1 (Figure 4B), which have known roles in tissue fibrosis and wound healing (Lee et al., 2011; Naveau et al., 1994; Wang et al., 2019a), were detected uniquely in the aged discs.

Shift of ECM composition and cellular responses in the outer AF A comparative analysis of the protein profiles indicated that the aged OAF retained 55% of the proteins of a young OAF. These changes are primarily reflected in the class of SLRPs (BGN, DCN, FMOD, OGN, and PRELP), and glycoproteins such as CILP, CILP2, COMP, FGA/B, and FGG. From a cellular perspective, 45 proteins enriched in the aged OAF could be classified under ‘responses to À stress’ (FDR q = 1.86 Â 10 7; contributed by CAT, PRDX6, HSP90AB1, EEF1A1, TUBB4B, P4HB, PRDX1, HSPA5, CRYAB, HIST1H1C), suggesting OAF ECM and cells are responding to a changing environment such as mechanical loading and other stress factors.

Convergence of the inner disc and outer regions in aged discs To map the relative changes between inner and outer regions of the aged discs, we performed a systematic comparison between compartments (Figure 4—figure supplement 1A–D). The most significant observation was a weakening of the distinction between IAF and OAF that was seen in young discs, with only 17 DEPs expressed higher in the OAF of the aged disc (Figure 4—figure supplement 1C). More differences were seen when we included the NP in the comparison with OAF (Figure 4—figure supplement 1D), indicating some differences remain between inner and outer regions of the aged discs. While the protein profiles of the NP and IAF were similar, with no detectable DEPs (Figure 4—figure supplement 1A & B), their compositions shared more resemblance with the OAF. These progressive changes of the protein profiles and DEPs between inner and outer compartments suggest the protein composition of the inner disc compartments becomes more

Tam, Chen, et al. eLife 2020;9:e64940. DOI: https://doi.org/10.7554/eLife.64940 13 of 37 Tools and resources Computational and Systems Biology

A CD Module Y1: NP markers Module Y2: AF markers P A P L RA P R L LL 2 1 P R 1 0 R A 0.5 L A R 0 0 −1 P í Z-scores (L3/4) R LP −1 L_OAFL_IAFL_NP/IAFNP R_NP/IAFR_IAFR_OAF R PC2 (21.8%) −2

A PC2 (21.8%) NP

Z-scores (all levels mixed)Z-scores (all L_OAFL_IAFL_NP/IAFNP R_NP/IAFR_IAFR_OAF NP/IAF L L3/4 R IAF L L R L4/5 2 L5/S1 OAF 0.5 −30 −20 −10 0 10 20

−30 −20 −10 0 10 20 í 0 A 1 −60 −40 −20 0 20 −60 −40 −20 0 20 0 PC1 (46.7%) PC1 (46.7%) 2 −1

Z-scores (L4/5) −2 1 L_OAFL_IAFL_NP/IAFNP R_NP/IAFR_IAFR_OAF

B 0

−1 2 Higher in OAF PKM −2 1 MSN levels mixed)Z-scores (all A_OAF A_NP/IAFNP P_NP/IAFP_OAF Higher in inner disc 0 −1

Z-scores (L5/S1) −2 ANXA6 L_OAFL_IAFL_NP/IAFNP R_NP/IAFR_IAFR_OAF AHNAK IQGAP1 GSTP1 E HSP90AA1 Module Y3: smooth muscle YWHAE F HBD MYH9 cell markers Module Y4: immune & blood HBB TKT 1.5 EEF1A1 HSP90AB1 1 ANXA1 HSPA1B 1 COL6A2 0.5 YWHAZ 0 0 TUBA1B CHI3L2 FBN1 RNH1 −0.5 PRDX1 ANXA4 CFL1 Z-scores (L3/4) −1 PGK1 −1 Z-scores (L3/4) ENO1 LOX PFN1 L_OAFL_IAFL_NP/IAFNP R_NP/IAFR_IAFR_OAF ANXA2 TUBB4B L_OAFL_IAFL_NP/IAFNP R_NP/IAFR_IAFR_OAF !"(FDR q) HBA1 S100A9 CTSD CLIC1 PPIA LMNA A2M FSCN1 BLVRB CA1 −log ANXA5 MAMDC2 PRDX6 COL1A1 UBA1 CAT CAPG SOD1CALR SERPING1 TPI1 COL6A3 PEBP1 2 COL6A1 PRDX2 EEF2 ACTN4 SELENBP1 2 UGP2 HIST1H1C VCP HIST2H2AC 1 RPS27A ACTG1 HIST1H2BL TNXB 1 ATRN THBS4 VIM ATP5A1 ANGPTL4 ARHGDIA CALM3 CAP1 COL12A1 0 0 HSP90B1 PLS3 LDHB ATP5B FLNA −1 C1S HSPG2 DSTN HNRNPA2B1 Z-scores (L4/5) −1 PDIA6 PARK7 TPM3 PLEC VASN HSPA5 YWHAQ NID2 GDI2 Z-scores (L4/5) RBP4 GAPDH CRYAB PTRF HSPA8 −2 PDIA3 AK1 L_OAFL_IAFL_NP/IAFNP R_NP/IAFR_IAFR_OAF L_OAFL_IAFL_NP/IAFNP R_NP/IAFR_IAFR_OAF CFHR2 ARF1 P4HB GPI TAGLN2 S100A4 LAMA5 ACTA2

2 2

0 2 4 6 1 1 0 0 −1

−2 0 2 4 6 Z-scores (L5/S1) −1

Z-scores (L5/S1) −2 L_OAFL_IAFL_NP/IAFNP R_NP/IAFR_IAFR_OAF log#(fold-change) L_OAFL_IAFL_NP/IAFNP R_NP/IAFR_IAFR_OAF

Figure 4. Characterisation of the aged cadaveric discs’ static spatial proteome. (A) Principal component analysis (PCA) plot of all the aged profiles on PC1 and PC2, colour-coded by compartments. Curves in the left panel show the support vector machine (SVM) boundaries between OAF and inner disc; those in the right panel separate the L5/S1 disc from the upper disc levels. Letters on dots indicate directions: L, left; R, right; A, anterior; P, posterior. (B) Volcano plot showing the differentially expressed proteins (DEPs) between the OAF and inner disc (an aggregate of NP, NP/IAF, and IAF), with the coloured dots representing statistically significant (FDR < 0.05) DEPs. (C) Using the same four modules identified in young samples, we determined the trend for these in the aged samples. Locational trends of module Y1 showing higher expression in the inner disc, albeit they are more flattened than in the young disc samples. Top panel shows left to right direction and bottom panel shows anterior to posterior direction. The red curve is the Gaussian Process Estimation (GPE) trendline, and the blue curves are one standard deviation above or below the trendline. This also applies to (D), (E), and (F). (D) Lateral trends for module Y2 in the aged discs. (E) Lateral trends for module Y3 in the aged discs. (F) Lateral trends for module Y4 in the aged discs. The online version of this article includes the following figure supplement(s) for figure 4: Figure supplement 1. Differentially expressed proteins (DEPs) between different compartments or levels and spatial trends of protein modules in the aged discs.

Tam, Chen, et al. eLife 2020;9:e64940. DOI: https://doi.org/10.7554/eLife.64940 14 of 37 Tools and resources Computational and Systems Biology similar to the OAF with ageing, with the greatest changes in the inner regions. This further supports a change initiating from the inner region of the discs with ageing.

Changes in young module patterns reflect convergence of aged disc compartments We investigated protein composition in the young disc modules (Y1-Y4) across the lateral and anteroposterior axes in the aged discs (Figure 4C–F). For module Y1 that consists of proteins defining the NP region, the distinctive concave pattern has flattened along both axes, but more so for the anteroposterior direction where the clear interface between IAF and OAF was lost (Figure 4C; Fig- ure 4—figure supplement 1I). Similarly for modules Y2 and Y3, which consist of proteins defining the AF region, the trends between inner and outer regions of the disc have changed such that the patterns become more convex, with a change that is a continuum from inner to outer regions (Figure 4D,E; Figure 4—figure supplement 1J,K). These changes in modules Y1-3 in the aged disc further illustrate the convergence of the inner and outer regions, with the NP/IAF becoming more OAF-like. For Y4, the patterns along the lateral and anteroposterior axes were completely disrupted (Figure 4F; Figure 4—figure supplement 1L). As Y4 contains proteins involved in vascularity and inflammatory processes (Supplementary file 5), these changes indicate disruption of cellular homeostasis in the NP.

Disc level variations reflect spatial and temporal progression of disc changes The protein profiles of the aged disc levels, consistent with the PCA findings, showed similarity between L3/4 and L4/5 (Figure 4—figure supplement 1E), but, in contrast to young discs, differences between L5/S1 and L4/5 (Figure 4—figure supplement 1F), and more marked differences between L5/S1 and L3/4 (Figure 4—figure supplement 1G,H). Overall, the findings from PCA, protein profiles (Figure 2B–D), and MRI (Figure 1C) agree. As compared to young discs, the more divergent differences across the aged disc levels potentially reflect progressive transmission from the initiating disc to the adjacent discs with ageing. To further investigate the aetiologies underlying IDD, cross-age comparisons are needed.

Aetiological insights uncovered by young/aged comparisons Next, we performed extensive pair-wise comparisons between the young and aged samples under a defined scheme (Figure 5—figure supplement 1A; Supplementary file 4). First, we compared all 33 young samples with all 33 aged samples, which identified 169 DEPs with 104 expressed higher in the young and 65 expressed higher in the aged discs (Figure 5A). A simple gene ontology (GO) term analysis showed that the most important biological property for a young disc is structural integrity, which is lost in aged discs (Figure 5B; Supplementary file 5). The protein classes most enriched in the young discs were related to cartilage synthesis, chondrocyte development, and ECM organisation (Figure 5B). The major changes in the aged discs relative to young ones, were proteins involved in cellular responses to an ageing environment, including inflammatory and cellular stress signals, progressive remodelling of disc compartments, and diminishing metabolic activities (Figure 5C; Supplementary file 5).

Inner disc regions present with most changes in ageing For young versus old discs, we compared DEPs of the whole disc (Figure 5A) with those from the inner regions (Figure 5D) and OAF (Figure 5E) only. Seventy-five percent (78/104) of the downregulated DEPs (Figure 5F) were attributed to the inner regions, and only 17% (18/104) were attributed to the OAF (Figure 5F). Similarly, 65% (42/65) of the upregulated DEPs were solely contributed by inner disc regions, and only 18.4% (12/65) by the OAF. Only 5 DEPs were higher in the OAF, while 49 were uniquely upregulated in the inner discs (Figure 5G). The key biological processes in each of the compartments are highlighted in Figure 5F and G.

The perturbed biological processes in the aged discs Expression of known NP markers was reduced in aged discs, especially proteins involved in the ECM and its remodelling, where many of the core matrisome proteins essential for the structural function

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A all 33 young vs all 33 aged B C Extracellular Structure Org Defense Rsps APO$ Skeletal Sys Dev Proteolysis Higher in aged Collagen Fibril Org Neg Reg Peptidase Higher in young Connective Tiss Dev

Adaptive Immune Rsps Bio Adhesion Neg Reg Proteolysis Cartilage Dev Reg Proteolysis Chondrocyte Di$ Reg Peptidase Bone Dev Neg Reg Hydrolase Cartilage Dev EC Structure Org Endochondral Bone Morph Endocytosis APOA1 Supramolecular Fiber Org IL17B &2/$ C9 Reg Hydrolase Activity &2/$ +63* Growth Plate Cartilage Dev IGHG1 Rsps To Wounding TF Bone Growth HP ) Wound Healing 0ATN3 FNDC1 APOH ALB Chondrocyte Dev IGK ,7,+ COL15A1 &2/$ C6 Fibrinolysis ++,3/ IGKC C8B PLG Skeletal Sys Morph THBS3 FGB FGG Reg Wound Healing FBN1 FGA Bone Morph TNXB COL5A1 (&0 (FDR q) (133 In#mtry Rsps 0A71 250 Growth Plate Cartilage Morph 10 VCAN F13A1 SCUBE1 ,7,+ Acute In#mtry Rsps &2/$ ITIH5 $0 IGLL5 HPX Chondrocyte Dev &2/$ ABI3BP APOE Cytolysis CLEC3A COL11A1 COL6A3 C3 AZGP1 GRC Chondrocyte Di$ COL3A1 LOX ,*+* SERPIND1 C5 −log CD109 APOB Reg Rsps To Stress COL6A1 CKB ANXA1 C8A Cartilage Morphogenesis HAPLN3 FRZB &% PA TTR SERPINC1 SPARC /(&7 CAPS 3*$0 $1*37/ HRG Phagocytosis CALU PCOLCE CFH CFI IGHA1 0 20 40 PRDX1 $1;$ S100A1 RCN3 CLU PYGB SERPINA1 & $)0 C7 VTN 0 10 20 30 1,' IGFBP7 SSC5D LO;/ (&0 35* POSTN 6(53,1) (0,/,1 COL8A1 SPP1 KRT19 ANG (0,/,1 HTRA1 IGLC6 -log (FDR q) &.0 KR7 $1;$ BGN LDHA FN1 CFB 003 10 XYLT1 /$0$ CYTL1 SERPINA3 HAPLN1 KRT8 PTRF ITGAV CCL18 PFKP CHAD -log (FDR q) 8*3 GNAS LYZ SERPINF1 GC 10 &+5'/ TNC 51$6( &% 7,03 LECT1 TUBA1B SLPI ACAN RCN1 VASNANXA5 ITIH1 CLSTN1 ATRN DSC3 TPI1 LOXL3 )02' 5%3 &2/$ &,/3 YWHAE 9,0 0), L7%3 FTL FLT1 CD9 3*0 VWA1 &2/$ LGALS3 3.0)*)%3 CTSD (+' GPC1 TGFBI GNB1 SERPINA5 0 5 10 15

0 6 8 í í Collagens: COL2A1, COL3A1, COL5A1, COL11A1, COL12A1, log (fold-change) COL14A1 Proteoglycans: ACAN, CHAD, HAPLN1 D Glycoproteins: ECM2, EMILIN1, EMILIN3, FNDC1, IGFBP7, LAMA5, Collagens: COL8A1 MATN2/3, NID2, PCOLCE, SPP1, THBS3, TNC, TNXB Higher in aged inner regions APOA4 F Proteoglycans: FMOD NP markers: KRT8/19, DSC3, LECT1/2, CHAD, HAPLN1, CLEC3A Higher in young inner regions Shh signalling: HHIPL2, SCUBE1 TNXB Glycoproteins: CILP2 COL11A2 COL6A3 HPX BMP/TGFβ antgaonists: FMOD Mesenchymal: VIM MATN3 THBS3 IGHG1 APOA1 Wnt, BMP/TGFβ antgaonists: FRZB, CHRDL2, CD109, COL5A1 HSPG2 IGHG2 Others: CD9, ITGAV, LOXL3, GPC1, GNAS FNDC1 CLU IGK C9 down-regulated IL17B COL6A2 ITIH5 ORM1 Inhibition of hypertrophy: S100A1 MATN2 HTRA1 ALB VIM ANXA2 in all aged COL5A2 COL6A1 PRDX1 A2M SERPINF1 FGG HHIPL2PTRF CD109 samples COL3A1 PRG4 IGKC F2 ENPP2 COL11A1NID2 IGLL5 PLG FBN1 ECM1 APOH SERPINC1 Collagens: COL1A2, COL5A2, COL6A1/2/3, (FDR q) CKM COL15A1 TF FGB XYLT1 FN1 AZGP1 HP C8B 10 ASPN TIMP3 FGA COL11A2, COL15A1 SCUBE1 TNC ANXA1 ITIH4 ITIH2 Proteoglycans: ASPN, CHADL UGP2 LOX COL1A2 COL2A1 CYTL1 TPI1 RNASE4 C3 IGHA1 Proteoglycans: BGN, HAPLN3, HSPG2, VCAN 8 CLEC3A COL12A1 PCOLCE C6 Glycoproteins: EDIL3, MXRA5, PCOLCE2, SMOC2, THBS4 KRT8 CALU SERPINA5ECM2PKM F13A1 −log FRZB YWHAE AFM ANXA4 ACTA2 EMILIN1 RBP4 Glycoproteins: ABI3BP TUBA1B CXCL12 ATRN Stress: HSP90AA1, HSPA1B, HSPA5, HSPA8, SOD1 CHADL SSC5DPGAM1 ABI3BP

5KRT19 COL14A1 10ALDOA PEBP1 QSOX1 ANGPTL2 15 SPP1 DSC3 MFI2PPIB TNFRSF11B CALM3 THBS4 C7 Others: FBN1, LOX, ANXA1/4/5, ANXA5, IL17B LECT1CLSTN1 CRYAB C5 0 78 LAMA5 CHAD HTRA3 Ribosomes: RPS27A, RNASE3 LMNAVCAN LYZ LOXL2 LECT2 SLPI CCL18ANGCSPG4 EDIL3 CCDC80 C2 HSPA1B ANXA6 EMILIN3 CKB MXRA5 LDHA CHRDL2HAPLN3 S100A1 RCN3 SERPINA3 APOE LGALS3 EEF1A1GAPDH TUBB4B ANXA5 IGLC6 MYH1 DKK3 CHST14 PGK1 SERPINA1 Wnt, BMP/TGFβ antgaonists: DKK3 HNRNPA2B1 EHD2 AHNAK CAPS SMOC2 CFD APOB 18 IGFBP7 DPYSL2 HAPLN1 PPIA PYGB SERPINE1 KRT14 MDH1GNB1PRDX6ENO1 TIMP1 FHL1 CLEC3B TKT RCN1RPS27A CFH IQGAP1 PCOLCE2 PXYLP1 CFB RNASE3PYGM HSP90AA1 BGN ACANHSPA5 TGFBI CTSD C4B HIST1H1C CDH1 PGM1 VASNLTBP4 FABP3 MYL1APOD FGFBP2 CLEC11A 1 YWHAZ DSTN P4HA1 HSPA8 57 PDGFRL AGT SOD1 ATP5A1 PFKP POSTN 2 ACTN1 Collagens: COL1A1 0 −5 0 5 10 down-regulated down-regulated log (fold-change) in aged SPARC, in aged E OAF VWA1 inner disc FBN1 BGN Higher in aged OAF Higher in young OAF G ANXA4 Proteoglycans: PRG4 Glycoproteins: ECM1, FGA, FGB, FGG, FN1 APOA4 ECM regulators: HRG, ITIH1, LOX APOH Immunoglobulins: IGHA1, IGHG1, IGHG2, IGK, IGKC, IGLC6, IGLL5, COL1A2 SERPIND1, SERPINF2 up-regulated COL15A1 Matrix remodelling: HTRA1, SERPINA1, SERPINA3, SERPINC1, SERPINF1, TIMP3 COL1A1 Others: TTR, C4BPA, C8A, MMP2 in all aged Blood: FGA, FGB, FGG, C2, C3, C4B, C5, C7, C8B, CFB, CFH COL6A2 ANXA1 CFI, FLT1, FTL, GC SPARC VWA1 samples Stress: CLU (FDR q) COL6A1 IL17B ANXA5 C6 VTN

10 Fibrosis: FN1, APOA1, APOB VCAN CTSD APOE COL5A2 F13A1 HAPLN3 HSPG2 C9 COL6A3 POSTN ABI3BP Matrix: Vtn 11 −log COL11A2 AHSG PLG IGHM APCS ITIH4 Proteolysis: MMP2 2 42 Blood: F13A1, C6, C9 10 Fibrosis: POSTN, APOA4, APOE 3 Immunoglobulins: IGHM 0 7 ECM a&liated: CLEC11A 0 1 2 3 4 Glycoproteins: AHSG, APCS ECM regulators: AGT, HTRA3, SERPINE1, TIMP1 −4 −2 0 2 4 6 8 up-regulated up-regulated Protelytic: HTRA3, TIMP1 log (fold-change) in aged in aged OAF inner disc H

protein protein min max I Not detected Young proteome Aged-Young & modules modules Enrichment Young Aged Fibrosis Ages M1a Adipogenesis Y1 Androgen response L3/4 L OAF Angiogenesis L3/4 P OAF L3/4 R OAF M1b Apical junction L4/5 A OAF L3/4 A OAF Apoptosis L5/S1 A OAF Coagulation L5/S1 P OAF 4 Y2 L5/S1 R OAF 2b L4/5 L OAF M2a L4/5 R OAF Complement L5/S1 L OAF L4/5 P OAF E2F targets L3/4 L IAF L3/4 L NP/IAF Y3 L3/4 NP EMT L3/4 P NP/IAF L3/4 R NP/IAF M2b Estrogen response early L3/4 R IAF Y4 L4/5 L IAF Estrogen response late L3/4 A NP/IAF Fatty acid metabolism L4/5 L NP/IAF L4/5 P NP/IAF G2M checkpoint L4/5 R IAF 1a 1b Glycolysis L5/S1 L IAF variable proteins L4/5 R NP/IAF Hedgehog signaling L4/5 NP Heme metabolism L4/5 A NP/IAF Hypoxia

L5/S1 A NP/IAF Other L5/S1 R IAF Il2 stat5 signaling L5/S1 L NP/IAF KRAS signaling dn L5/S1 R NP/IAF L5/S1 NP KRAS signaling up L5/S1 P NP/IAF Mitotic spindle L3/4 L OAF MTORC1 signaling L3/4 R OAF MYC targets v1 L3/4 A OAF 2a M3 L4/5 L OAF 3 L5/S1 R OAF L3/4 P OAF Myogenesis L3/4 R IAF L3/4 L IAF L3/4 A NP/IAF L3/4 P NP/IAF Oxidative phosphorylation

L3/4 R NP/IAF the constant set P53 pathway L3/4 L NP/IAF 6 Peroxisome

L3/4 NP Proteins in L4/5 L NP/IAF PI3K/AKT MTor signaling L4/5 NP L4/5 P NP/IAF Protein secretion L4/5 R IAF ROS pathway L4/5 L IAF UV response dn L4/5 A NP/IAF L4/5 R NP/IAF M4 Xenobiotic metabolism L5/S1 P NP/IAF Hypertrophy L4/5 A OAF 6a L4/5 R OAF M5 Stress L4/5 P OAF L5/S1 P OAF L5/S1 L OAF L5/S1 A OAF L5/S1 L IAF M6a L5/S1 R IAF

L5/S1 L NP/IAF 5 Marginally Others

L5/S1 NP detected L5/S1 R NP/IAF in young L5/S1 A NP/IAF M6b proteins ZG16 IGKV1-33 IAH1 NOV 0*$7 CTNNB1 3/$*$ ITIH6 SAA1 PRPH PREP SPAG9 EPYC DAG1 SSBP1 03= PPBP $2& VPS13C 52&. 0<$'0IGHV5-51 FLT1 CDSN 3$366 HK1 KRT6C KRT5 KRT9 KRT1 .57 TSN 360' SPARC OXCT1 NES )%;2 ATP1A1 7:) NEO1 SRPX 300 3*$0 /(&7 LANCL1 &+5'/ SSC5D SELP RCN1 THBS3 FNDC1 COL8A1 RNASE3 COL3A1 /7%3 TNXB HRAS COL15A1 S100A6 S100A1 SPP1 360% VAPA 51$6(7 IGLV6-57 HHIP CTSC .57 0$71 HYAL1 ()+' DDB1 CAPN3 BCAN SLPI TNFRSF11B XYLT1 SERPINA5 0), CLSTN1 &;&/ SCUBE1 VIT IL17B 0$71 &2/$ FRZB ITIH5 COL5A1 CD109 (133 (0,/,1 COL9A1 LOXL3 /2;/ LECT1 IGFBP7 51$6( LYZ CHAD )*)%3 ANG VCAN ABI3BP ++,3/ &2/$ &2/$ COL11A1 CLEC3A SLIT3 KRT8 KRT19 0;5$ DKK3 INHBA QSOX1 APOD CDH1 ITGAV CKB RNASE1 CD9 TPR LPL STXBP3 0<2= COL9A3 UBXN10 0*3 ECH1 SRL GRN 05& EEA1 LGALS3 8*3 DSC3 EPHX1 %03 360( CCL18 SLC1A5 G6PD **7 $32%(& ITGB1 FBN3 '33 RSU1 KAL1 %*$7 &&7 ACADVL 32)87 3/,1 /0$1 GYG1 +63$ /0$1 )$0% SPTA1 HBG1 ANGPTL7 SPTB S100A8 ERAP1 LASP1 FIBIN HSPA13 AKR1C1 RYR1 LTBP1 UTRN 6/&$ VEGFA GNA11 FKBP7 0(&3 ATP6V1E1 &2/$ SEC31A PDGFD KRT16 )$0% IGFBP6 H1FX ,*+9 &5,3 CFHR1 PTN 0603 EIF5 ESYT1 DKK1 XIRP1 360$ HPRT1 CAPN13 DHX9 COPA $30$3 SBSPON 710' $32& 0$63 CASQ1 =1) DEFA3 07$ HIST1H1B NT5E STXBP1 $0%3 KERA KRT10 FHL3 COL1A1 :,63 FBLN1 0<2& CAV1 TNNI1 TOR1B 6/&$ GOT1 3<*0711& TNNC1 0<+ TNNT3 0

Figure 5. Comparisons between young and aged static spatial proteomes. (A) Volcano plot showing the differentially expressed proteins (DEPs) between all the 33 young and 33 aged profiles. Coloured dots represent statistically significant DEPs. (B) Gene ontology (GO) term enrichment of DEPs higher in young profiles. (C) GO term enrichment of DEPs higher in aged profiles. Full names of GO terms in (B and C) are listed in Supplementary file 5.(D) Volcano plot showing DEPs between aged and young inner disc regions. (E) Volcano plot showing DEPs between aged and young OAF. (F) Venn Figure 5 continued on next page

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Figure 5 continued diagram showing the partitioning of the young/aged DEPs that were downregulated in aged discs, into contributions from inner disc regions and OAF. (G) Venn diagram showing the partitioning of the young/aged DEPs that were upregulated in aged discs, into contributions from inner disc regions and OAF. (H) A heat map showing proteins expressed in all young and aged disc, with the identification of 6 modules (module 1: higher expression in young inner disc regions, modules 2 and 4: higher expression in young OAF, module 3: highly expressing in aged OAF, module 5: higher expression across all aged samples, and module 6: higher expression in aged inner disc, and some OAF). (I) An alluvial chart showing the six modules identified in (H) and their connections to the previously identified four modules and constant set in the young reference proteome; as well as their connections to enriched GO terms. The online version of this article includes the following figure supplement(s) for figure 5: Figure supplement 1. Comparisons of young and aged proteomes.

of the NP were less abundant or absent. While this is consistent with previous observations (Feng et al., 2006), of interest was the presence of a set of protein changes that were also seen in the OAF, which was also rich in ECM and matrix remodelling proteins (HTRA1, SERPINA1, SER- PINA3, SERPINC1, SERPINF1, and TIMP3) and proteins involved in fibrotic events (FN1, POSTN, APOA1, APOB), suggesting these changes are occurring in the ageing IVDs (Figure 5F,G). Proteins associated with cellular stress are decreased in the aged inner disc, with functions ranging from molecular chaperones needed for protein folding (HSPB1, HSPA1B and HSPA9) to modula- tion of oxidative stress (SOD1) (Figure 5F). SOD1 has been shown to become less abundant in the aged IVD (Hou et al., 2014) and in osteoarthritis (Scott et al., 2010). HSPB1 is cytoprotective and a deficiency is associated with inflammation reported in degenerative discs (Wuertz et al., 2012). We found an increased concentration of clusterin (CLU) (Figure 5G), an extracellular chaperone that aids the solubilisation of misfolded protein complexes by binding directly and preventing protein aggre- gation (Trougakos, 2013; Wyatt et al., 2009), and also has a role in suppressing fibrosis (Peix et al., 2018). Inhibitors of WNT (DKK3 and FRZB), and antagonists of BMP/TGFb (CD109, CHRDL2, DCN FMOD, INHBA and THBS1) signalling were decreased or absent in the aged inner region (Figure 5F, G), consistent with the reported upregulation of these pathways in IDD (Hiyama et al., 2010) and its closely related condition osteoarthritis (Leijten et al., 2013). Targets of hedgehog signalling (HHIPL2 and SCUBE1) were also reduced (Figure 5F), consistent with SHH’s key roles in IVD development and maintenance (Rajesh and Dahia, 2018). TGFb signalling is a well-known pathway associated with fibrotic outcomes. WNT is known to induce chondrocyte hypertrophy (Dong et al., 2006), that can be enhanced by a reduction in S100A1 (Figure 5F), a known inhibitor of chondrocyte hypertrophy (Saito et al., 2007). To gain an overview of the disc compartment variations between young and aged discs, we followed the same strategy as in Figure 3—figure supplement 2 to aggregate three categories of DEPs in all 23 comparisons (Figure 5—figure supplement 1A) and created a heatmap from the resulting 719 DEPs. This allowed us to identify six major protein modules (Figure 5H). A striking feature is module 6 (M6), which is enriched for proteins involved in the complement pathway (GSEA À À Hallmark FDR q = 4.9 Â 10 14) and angiogenesis (q = 2.3 Â 10 3). This module contains proteins that are all highly expressed in the inner regions of the aged disc, suggesting the presence of blood. M6 also contains the macrophage marker CD14, which supports this notion. We visualised the relationship between the young (Y1-4) and young/aged (M1-6) modules using an alluvial chart (Figure 5I). Y1 corresponds primarily to M1b that is enriched with fibrosis, angiogenesis, apoptosis, and EMT (epithelial to mesenchymal transition) proteins. Y2 seems to have been deconstructed into three M modules (M2b > M3 > M4). M2b and M3 contain proteins linked to het- erogeneous functions, while proteins in M4 are associated with myogenesis and cellular metabolism, but also linked to fibrosis and angiogenesis. Y3 primarily links to M2a with a strong link to myogenesis, and mildly connects with M3 and M4. Y4 has the strongest connection with M6a, which is linked to coagulation. Both the variable and the constant sets of the young disc were also changed in ageing. In the constant set, there is a higher tendency for a decrease in ECM-related proteins, and an increase in blood and immune related proteins with ageing, that may reflect an erosion of the foun- dational proteome, and infiltration of immune cells (Figure 5—figure supplement 1L).

Tam, Chen, et al. eLife 2020;9:e64940. DOI: https://doi.org/10.7554/eLife.64940 17 of 37 Tools and resources Computational and Systems Biology In all, the IVD proteome showed that with ageing, activities of the SHH pathways were decreased, while those of the WNT and BMP/TGFb pathways, EMT, angiogenesis, fibrosis, cellular stresses, and chondrocyte hypertrophy-like events were increased.

Comparison between transcriptomic and proteomic data The proteome reflects both current and past transcriptional activities. To investigate upstream cellular and regulatory activities, we obtained transcriptome profiles from two IVD compartments (NP and AF) and two sample states (young, scoliotic but non-degenerated, YND; aged individuals with clinically diagnosed IDD, AGD) (Table 1). The transcriptome profiles of YND and AGD are similar to the young and aged disc proteome samples, respectively. After normalisation (Figure 6—figure supplement 1A) and hierarchical clustering, we found patterns reflecting relationships among IVD compartments and ages/states (Figure 6—figure supplement 1B–D). PCA of the transcriptome profiles showed that PC1 captured age/state variations (Figure 6A) and PC2 captured the compartment (AF or NP) differences, with a high degree of similarity to the proteomic PCA that explained 65.0% of all data variance (Figure 2A).

Transcriptome shows AF-like characteristics of the aged/degenerated NP We compared the transcriptome profiles of different compartments and age/state-groups (Fig- ure 6—figure supplement 1E–H). We detected 88 DEGs (Materials and methods) between young AF and young NP samples (Figure 6—figure supplement 1E),in which 39 were more abundant in young NP (including known NP markers CD24, KRT19) and 49 were more abundant in young AF, including the AF markers, COL1A1, and THBS1. In the AGD samples, 11 genes differed between AF and NP (Figure 6—figure supplement 1F), comparable to the proteome profiles (Figure 4C). Between the YND and AGD AF, there were 45 DEGs, with COL1A1 and MMP1 more abundant in YND and COL10A1, WNT signalling (WIF1, WNT16), inflammatory (TNFAIP6, CXCL14, IL11), and fibrosis-associated (FN1, CXCL14) genes more abundant in AGD (Figure 6—figure supplement 1G). The greatest difference was between YND and AGD NP, with 216 DEGs (Figure 6—figure supplement 1H), with a marked loss of NP markers (KRT19, CD24), and gain of AF (THBS1, DCN), proteolytic (ADAMTS5), and EMT (COL1A1, COL3A1, PDPN, NT5E, LTBP1) markers with age. Again, consistent with the proteomic findings, the most marked changes are in the NP, with the transcriptome profiles becoming AF-like.

Concordance between transcriptome and proteome profiles We partitioned the DEGs between the YND and AGD into DEGs for individual compartments (Figure 6B). The transcriptomic (Figure 6B) Venn diagram was very similar to the proteomic one (Figure 5F–G). For example, WNT/TGFb antagonists and ECM genes were all downregulated with ageing/degeneration, while genes associated with stress and ECM remodelling were more common. When we directly compared the transcriptomic DEGs and proteomic DEPs across age/states and compartments (Figure 6C–F), we observed strong concordance between the two types of datasets for a series of markers. In the young discs, concordant markers included KRT19 and KRT8, CHRDL2, FRZB, and DKK3 in the NP, and COL1A1, SERPINF1, COL14A1, and THBS4 in the AF (Figure 6C). In the AGD discs, concordant markers included CHI3L2, A2M and ANGPTL4 in the NP and MYH9, HSP90AB1, HBA1, and ACTA2 in the AF (Figure 6D). A high degree of concordance was also observed when we compared across age/states for the AF (Figure 6E) and NP (Figure 6F). Despite the transcriptomic samples having diagnoses (scoliosis for YND and IDD for AGD), whereas the proteome samples were cadaver samples with no reported diagnosis of IDD, the changes detected in the transcriptome profiles substantially support the proteomic findings. A sur- prising indication from the transcriptome was the increased levels of COL10A1 (Lu et al., 2014), BMP2 (Grimsrud et al., 2001), IBSP, defensin beta-1 (DEFB1), ADAMTS5, pro-inflammatory (TNFAIP6, CXCL) and proliferation (CCND1, IGFBP) genes in the AGD NP (Figure 6—figure supplement 1E–H), reaffirming the involvement of hypertrophic-like events (Melas et al., 2014) in the aged and degenerated NP. The genome-wide transcriptomic data included over 20 times more genes per profile than the proteomic data, providing additional biological information about the disc, particularly low

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A Collagens: COL1A1, COL3A1,

-60YND74NP -40 -20 0 20 40 116 35 0 0 -100 -50 0 50 100 down-regulated down-regulated up-regulated up-regulated PC1 (51.5%) in AGD AF in AGD NP in AGD AF in AGD NP

C D LAMC1 0<+ SERPINF1

HPX SERPINC1 $+1$. IGHA1 MYH9 FGG ITIH2 A73% LAMA4 9&3 THBS1 /01$ PLS3 COL1A1 CAP1 AOC2 ,4*$3 VCPATP5BSERPINH1COL14A1 CALR VCLACTN1 COL12A1 +63$% CALR THBS4 +63$$ NCL 78%$% +%% +%$ COL6A1THBS2 +63A8 $1;$ +1513$% ,4*$3 &/,& *673 MFGE8 AC7A2 35'; $1;$ 3)1 $1;$ 61' GDI2 &2/$ +,67+& *1$, A&71 8%$ 33,$ EEF2 35(/3 A&7* ALDOAALD OA GDI2 $1;$ FMOD $1;$ A&7* 7+%6 653; $5) VWVW LLOO 62' $5) $ ; (()$ YWHAE '&1 +63% 33,$&203 '671 3',$ 8*3 YWHAE 3',$ &,/3 35'; RXQJLQQHUGLVF` 3',$ *$3'+ )%1 $1;$ 061 7*)%, YWHAZ 061 +63%

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3&2/&( &)% &&'& 602& /3/ $)`í^ CHRD 602& 7,03 *$/17 EDIL3 SURWHRPLFVGDWD SURWHRPLFVGDWD &' MFI2 OOAFAF O SLPI .$/ &6 6(53,1( FRZB 6(53,1$ &2/$ CP CLSTN1 CHRDL2 MXRA5 SEMA3C QSOX1 DKK3 MATN3 TNFRSF11B V$61 RXQJ &6 ^DJHG2$)`í^DJHGLQQHUGLVF` {y

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^<1'$)`í^<1'13` ^$*'$)`í^$*'13` (microarray data) (microarray data) E F IGHM

APOA4 APOA4 SERPINC1 APOA1 HPX PLG FGG IGHA1 FGA PLG ITIH2 ITIH4 F2 POSTN C8B SERPINF1 VTN ITIH4 ORM1 C6 APOB APCS IGHG1 C6 APOH AZGP1 TF AHSG F13A1 PRG4 POSTN F13A1 A2M ECM1

&76' TIMP3 +75$ 6(53,1$ +75$ )1 CLU 6(53,1$ CFD 6(53,1( &76' &&'& $1*37/ 7,03 &/(&$ SURWHRPLFVGDWD SURWHRPLFVGDWD +6+63$3$ $1;$ $32' EDIL3 +63$$ A&$1 536$ 3(%3 $1;$ +151+1513$%3$% YWHAE 7+%6 AACC A2 5&1 '671 VVWW$ +,67+& 7 /*$/6 633 $%,%3 +$3/1+$3/1+$3/1 $1;$ &2/$ CCL18CXCL12PYGM &2/$ AHNAK LECT1 TUBA1B ^DJHG2$)`í^\RXQJ2$)` COL1A1 +$3/1 &2/$ CD109 COL6A1 &2/$ CHRDL2 SLPI CALU VIM COL6A1 FRZB ANXA4 UGP2 SCUBE1 SPARC CLEC3A COL14A1 ^DJHGLQQHUGLVF`í^\RXQJLQQHUGLVF` ENPP2 CKM COL11A2

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−4 −2 0 2 4 6 8 KRT19 IL17B IL17B COL11A2

í í 2 í í 2

{AGD $)`í^<1' AF} ^$*'13`í^<1'13` (microarray data) (microarray data)

Figure 6. Concordance between static spatial proteomic and transcriptome data. (A) A principal component analysis (PCA) plot of the eight transcriptomic profiles. Curves represent support vector machine (SVM) boundaries between patient-groups or compartments. (B) Venn diagrams showing the partitioning of the young/aged DEGs into contributions from inner disc regions and OAF. Left: downregulated in AGD samples; right: upregulated. (C) Transcriptome data from the NP and AF of two young individuals were compared to the proteomic data, with coloured dots Figure 6 continued on next page

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Figure 6 continued representing identified proteins also expressed at the transcriptome level. (D) Transcriptome and proteome comparison of aged OAF and NP. (E) Transcriptome and proteome comparison of young and aged OAF. (F) Transcriptome and proteome comparison of young and aged NP. The online version of this article includes the following figure supplement(s) for figure 6: Figure supplement 1. Microarray transcriptomic data of the NP and AF from four individuals, two of which are young and non-degenerated and the other two aged and degenerated.

abundance proteins, such as transcription factors and surface markers. For example, additional WNT antagonists were WIF1 (Wnt inhibitory factor) and GREM1 (Figure 6B Leijten et al., 2013). Compar- ing the YND NP against YND AF or AGD NP (Figure 6—figure supplement 1E,H), we identified higher expression of three transcription factors, T (brachyury), HOPX (homeodomain-only protein homeobox), and ZNF385B in the YND NP. Brachyury is a well-known marker for the NP (Risbud et al., 2015), and HOPX is differentially expressed in mouse NP as compared to AF (Veras et al., 2020), and expressed in mouse notochordal NP cells (Lam, 2013). Overall, transcriptomic data confirmed the proteomic findings and revealed additional markers.

The active proteomes in the ageing IVD The proteomic data up to this point is a static form of measurement (static proteome) and represents the accumulation and turnover of all proteins up to the time of harvest. The transcriptome indicates genes that are actively transcribed, but does not necessarily correlate to translation or protein turnover. Thus, we studied changes in the IVD proteome (dynamic proteome) that would reflect newly synthesised proteins and proteins cleaved by proteases (degradome), and how they relate to the static proteomic and transcriptomic findings reported above.

Aged or degenerated discs synthesise fewer proteins We performed ex vivo labelling of newly synthesised proteins using the SILAC protocol (Ong et al., 2002; Figure 7A; Materials and methods) on AF and NP samples from four YND individuals and one AGD individual (Table 1). In the SILAC profiles, light isotope-containing signals correspond to the pre-existing unlabelled proteome, and heavy isotope-containing signals to newly synthesised proteins (Figure 7B). The ECM compositions in the light isotope-containing profiles (Figure 7B, middle panel) are similar to the static proteome samples of the corresponding age groups described above (Figure 1F). Although for NP_YND152, the numbers of identified proteins in the heavy profiles are considerably less than NP_YND151 due to a technical issue during sample preparation, it is overall still more similar to NP_YND152 than to other samples (Figure 7—figure supplement 1B), indicating that its biological information is still representative of a young NP and the respective AF samples are similar. In contrast, the heavy isotope-containing profiles contained fewer proteins in the AGD than in the YND samples (Figure 7B, left panel) and showed variable heavy to light ratio profiles (Figure 7B, right panel). To facilitate comparisons, we averaged the abundance of the proteins detected in the NP or AF for which we had more than one sample, then ranked the abundance of the heavy isotope-containing (Figure 7C), light isotope-containing (Figure 7D) proteins. The number of proteins newly synthesised in the AGD samples was about half that in the YND samples (Figure 7C). This is unlikely to be a technical artefact as the total number of light isotope-containing proteins detected in the AGD samples is comparable to the YND, in both AF and NP (Figure 7D), and the difference is again well illustrated in the heavy to light ratios (Figure 7—figure supplement 1A). Reduced synthesis of non-matrisome proteins was found for the AGD samples (GAPDH) as a reference point (dotted red lines) (Figure 7C and D; Figure 7C and E, grey portions). Of the 68 high abundant non-matrisome proteins in the YND NP compartment that were not present in the AGD NP, 28 are ribosomal proteins (Figure 7—figure supplement 1C), suggesting reduced translational activities. This agrees with our earlier findings of cellularity, as represented by histones, in the static proteome (Figure 1J,K). Changes in protein synthesis in response to the cell microenvironment affects the architecture of the disc proteome. To understand how the cells may contribute and respond to the accumulated matrisome in the young and aged disc, we compared the newly synthesised matrisome proteins of

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A B YND: Young and non-degenerated Collagens Glycoproteins ECM regulators Others SILAC AGD: Aged and degenerated Proteoglycans ECM affiliated Secreted factors

YND NP YND AF

250 300 150 200 AGDAGD NP AF

heavy ex vivo 150 200 100 labeling culture medium (7d) 100 100 50 50 Heavy-to-Light Ratio

0 Light (# proteins detected) 0 0 Heavy (# proteins detected) Heavy

NP_AGD80AF_AGD80 NP_AGD80AF_AGD80 NP_AGD80AF_AGD80 NP_YND151NP_YND152AF_YND151AF_YND152OAF_YND148OAF_YND149 NP_YND151NP_YND152AF_YND151AF_YND152OAF_YND148OAF_YND149 NP_YND151NP_YND152AF_YND151AF_YND152OAF_YND148OAF_YND149 AAAAA C SOD2 GAPDHTAGLN2 FN1 SOD3 PRG4 CLU COL2A1Caveolin FGFBP2 200 150 YND AF (averaged) 100 COMPCILP HAPLIN1HBB COL1A1 50 0 1 100 200 300 HBB CD44 C1QBGAPDH SOST COL2A1 FGFBP2 200 FN1 150 AGD AF

CILPA2M 100 CLU HAPLIN1 COMPHBA1 50 PRG4 0 1 100 200 300 light heavy GREM1TAGLN2 GAPDH SOD2 (existing) (newly synthesised) 200 150 COL2A1Caveolin YND NP (averaged) COL1A1FN1 100 HAPLIN1 CILP HBBCLU A2M Abundance (Heavy) Abundance 50 PRG4 0 1 100 200 300 GAPDHFN1 200 HBB COL2A1 AGD NP m/z 150 100 CLU HAPLIN1CILP A2M COMP 50 PRG4 HBA1HBA2 0 1 100 200 300 GAPDH proteins

D 200 E 150 YND AF (averaged) PLOD2 P4HA2 LOXL2 IGFBP7 P3H1 P4HA1 LGALS1 IGFBP3 SERPINH1 MMP3 COL5A2 SERPINE2 S100A10 COL5A1 AEBP1 COL3A1 PLOD1 F13A1 MGP AMBP COL11A1 PCOLCE ANXA2 FN1 TIMP3

200 COL9A3

100 ANXA1 EDIL3 SPARC CD109 ACAN THBS4 ANXA6 PRG4 VTN FBLN1 HTRA1 SMOC2 POSTN TNC COL2A1 FNDC1 MFAP4 NID1 COL6A2 S100A4 KERA S100A1 TGFBI THBS2 MFGE8 ANXA4 ITIH4 COL14A1

50 LUM THBS1 S100B CHAD LAMC1 FGFBP2 COL11A2 PTN FBN1 DCN 100 COL15A1 ABI3BP COL6A3 LOX ASPN COMP (Heavy) HSPG2 CILP SRPX2 HAPLN1 COL1A2 0 CLEC3A TNXB CILP2 in YND AF in FMOD COL6A1 COL1A1 COL12A1 Abundance MATN2 PRELP SERPINF1 FGG FGA C1QB ANGPTL2 1 100 200 300 400 SERPINF2 A2M ITIH2 PLG SERPIND1 0 200 150 AGD AF 100 100 (Heavy) in AGD AF in AGD 50 Abundance 200 0 1 100 200 300 400 Collagens Glycoproteins ECM regulators 200 Proteoglycans ECM affiliated Secreted factors 150 YND NP (averaged) 100 50 Abundance (Light) Abundance LGALS1 GREM1 P4HA2 P3H1 PLOD2 SERPINH1 P4HA1 LOXL2 FSTL1 ANXA2 ANXA6 S100A10 CSPG4 ASPN COL5A1 200 ANXA1 COL14A1 0 S100A1 COL5A2 TIMP3 S100A4 PLOD1 TNC COL9A3 COL6A2 MGP COL11A1 TGFBI COL2A1 SPARC EDIL3 SERPINE2 COL9A1 F13A1 THBS1 ANXA4 1 100 200 300 400 S100B AEBP1 ABI3BP CD109 COL1A1 FN1 HAPLN1 POSTN LUM LOX EMILIN1 CHAD COL1A2 MFGE8 100 COL11A2 COL6A3 ACAN HSPG2 200 MFAP4 SRPX2 CILP MATN4 (Heavy) CLEC3A FMOD A2M CILP2 DCN in YND NP in FBN1 HTRA1 COL6A1 SERPINF1 Abundance PRG4 ITIH2 PRELP COL12A1 IGFBP3 ECM2 VTN FGA FGG COMP PLG SERPINC1 SERPINF2 ITIH4 SERPINA1 KNG1 C1QC SERPINA5 LPA SERPIND1 150 AGD NP SEMA3A 0 100 50 100 0 (Heavy) in AGD NP in AGD 1 100 200 300 400 Abundance 200 proteins GAPDH F TMT TAILS G í 0 1 Reference Target

K K N K N K YND136AF N N TMT YND141AF labeling AGD65AF K K K K AGD62AF K K AGD143AF Pool AGD67AF K K K K HBB CILP CILP CILP CILP CILP FAT1 HBA1 MYLK ACAN ACAN ACAN ACAN ACAN ACAN ACAN CILP2 COMP COMP COMP COMP COMP COMP COMP COMP IgK V-I IgK V-I PHPT1 PHPT1 THBS3 PRELP

K K NEDD4 C9orf50 IgK V-III C3orf67 IgK V-III IgK V-III IgK V-IV COL1A1 COL2A1 COL1A1 COL1A2 COL1A2 COL1A2 COL1A2 COL2A1 COL1A1 COL2A1 COL2A1 COL2A1 COL2A1 COL2A1 COL2A1 COL6A1 COL1A1 COL2A1 COL2A1 COL2A1 COL1A2 COL1A1 COL1A1 COL1A1 COL1A1 COL1A1 COL1A1 COL1A1 COL1A1 COL1A1 COL1A1 COL1A1 COL2A1 COL1A1 COL2A1 COL1A2 COL1A1 COL2A1 COL2A1 COL2A1 COL1A1 COL1A1 COL1A2 COL1A1 COL1A1 COL1A1 COL1A1 COL1A1 COL1A1 COL1A1 COL1A1 COL1A2 COL1A2 COL2A1 COL2A1 COL2A1 COL2A1 COL2A1 COL2A1 COL2A1 COL2A1 COL2A1 COL1A1 COL2A1 COL2A1 COL2A1 COL2A1 COL2A1 COL2A1 COL1A2 COL2A1 COL1A1 COL2A1 HAPLN1 HAPLN1 FGFBP2 HAPLN1 ANAPC1 RECQL5 TNFAIP2 COL11A1 COL11A1 COL18A1 Ig heavy V-III Uncharacterized Trypsin digestion More degraded in YND AF (unique 13 proteins) More degraded in AGD AF (unique 24 proteins) K K H K K K K í 0 1 Depletion of CHO tryptic peptides CHO withpolymer YND141NP YND136NP AGD65NP AGD143NP Enrichment of AGD62NP N-terminal peptides & mass spectrometry AGD67NP C3 LYZ FN1 CLU CLU DSP A2M HBB CILP CILP CILP SZT2 SZT2 PLK3 HBA1 NOS2 PRG4 PRG4 KIF2C ASIC1 IgK V-I IgK V-I IgK V-I TULP3 SPG11 LRP1B LPHN3 IgK V-II IgK V-II IgK V-III IgK V-III IgK V-III ZNF708 IgK V-IV COL2A1 COL2A1 COL2A1 COL6A1 COL1A1 COL2A1 COL2A1 COL2A1 COL2A1 COL2A1 COL2A1 COL2A1 COL2A1 COL2A1 COL2A1 COL2A1 COL2A1 COL2A1 COL2A1 COL2A1 COL2A1 COL2A1 COL2A1 COL2A1 COL2A1 COL2A1 COL2A1 COL2A1 COL2A1 COL2A1 COL2A1 COL2A1 COL2A1 COL2A1 COL2A1 MED13L FGFBP2 HAPLN1 ACAD10 HAPLN1 DEPDC5 MALRD1 SPATA17 COL11A1 COL11A2 TMEM123 Ig heavy V-III Uncharacterized Uncharacterized m/z Uncharacterized More degraded in YND NP (unique 10 proteins) More degraded in AGD AF (unique 32 proteins)

Figure 7. The dynamic proteome of the intervertebral disc shows less biosynthesis of proteins and more degradative events in aged tissues. (A) Schematic showing pulse-SILAC labelling of ex-vivo cultured disc tissues where heavy Arg and Lys are incorporated into newly made proteins (heavy), and pre-existing proteins remaining unlabelled (light). NP and AF tissues from young (n = 3) and aged (n = 1) were cultured for 7 days in hypoxia prior to MS. (B) Barcharts showing the number of identified non-matrisome (grey) and matrisome (coloured) existing proteins (middle panel); newly Figure 7 continued on next page

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Figure 7 continued synthesised proteins (left panel), and the heavy/light ratio (right panel) for each of the samples. (C) The quantities of each of the heavy labelled (newly synthesised) proteins identified for each of the four groups were averaged, and then plotted in descending order of abundance. It shows that YND AF and NP synthesise higher numbers of proteins than the AGD AF and NP. The red dotted reference line shows the expression of GAPDH. (D) The quantities of each of the light (existing) proteins identified for each group was averaged, and then plotted in descending order of abundance which shows that there are similar levels of existing proteins in the four pooled samples. (E) The matrisome proteins of (C) were singled out for display. The abundance of these proteins in YND samples were generally higher across all types of matrisome proteins than the AGD, with the exceptions of aged related proteins. (F) Schematic showing the workflow of degradome analysis by N-terminal amine isotopic labelling (TAILS) for the identification of cleaved neo N-terminal peptides. (G) Heatmap showing the identification of cleaved proteins ranked according to tandem mass tag (TMT) isobaric labelling of N-terminal peptides in AF. Data is expressed as the log2(ratio) of N-terminal peptides. (H) Heatmap showing the identification of cleaved proteins ranked according to tandem mass tag (TMT) isobaric labelling of N-terminal peptides in NP. Data is expressed as the log2(ratio) of N-terminal peptides. AGD143 in (G and H) is aged but not degenerated (trauma). The online version of this article includes the following figure supplement(s) for figure 7: Figure supplement 1. SILAC data. Figure supplement 2. Degradome data.

AGD and YND samples rearranged in order of abundance (Figure 7E). More matrisome proteins were synthesised in YND samples across all classes. In YND AF, collagens were synthesised in higher proportions than in AGD AF with the exception of fibril-associated COL12A1 (Figure 7E, top panel). Similarly, higher proportions in YND AF were observed for proteoglycans (except FMOD), glycoproteins (except TNC, FBN1, FGG and FGA), ECM affiliated proteins (except C1QB), ECM regulators (except SERPINF2, SERPIND1, A2M, ITIH2, PLG), and secreted factors (except ANGPTL2). Notably, regulators that were exclusively synthesised in young AF are involved in collagen synthesis (P4HA1/ 2, LOXL2, LOX, PLOD1/2) and matrix turnover (MMP3), with enrichment of protease HTRA1 and protease inhibitors TIMP3 and ITIH in YND AF compared to AGD AF. In AGD NP, overall collagen synthesis was less than in YND NP (Figure 7E, lower panel); however, there was more synthesis of COL6A1/2/3 and COL12A1. Furthermore, AGD NP synthesised more LUM, FMOD, DCN, PRG4, and PRELP proteoglycans than YND NP. Notably, there was less synthesis of ECM-affiliated proteins (except C1QC and SEMA3A) and regulators – particularly those involved in collagen synthesis (P4HA1/2, LOXL2, LOX) – but an increase in protease inhibitors. A number of newly synthesised proteins in AGD NP were similarly represented in the transcriptome data, including POSTN, ITIH2, SERPINC1, IGFBP3, and PLG. Some genes were simultaneously underrepresented in the AGD NP transcriptome and newly synthesised proteins, including hypertrophy inhibitor GREM1 (Leijten et al., 2013).

Proteome of aged or degenerated discs is at a higher degradative state The degradome reflects protein turnover by identifying cleaved proteins in a sample (Lo´pez- Otı´n and Overall, 2002). When combined with relative quantification of proteins through the use of isotopic and isobaric tagging and enrichment for cleaved neo amine (N)-termini of proteins before labelled samples are quantified by mass-spectrometry, degradomics is a powerful approach to identify the actual status of protein cleavage in vivo. We employed the well-validated and sensitive terminal amine isotopic labelling of substrates (TAILS) method (Kleifeld et al., 2010; Rauniyar and Yates, 2014) to analyse and compare six discs from six individuals (two young and non-degenerated, YND; and four aged and/or degenerated, AGD) (Table 1; Figure 7F; Kleifeld et al., 2010; Rauniyar and Yates, 2014) using the 6-plex tandem mass tag (TMT)-TAILS (labelling six independent samples and analysed together on the mass spectrometer) (Figure 7F). Although shotgun proteomics is intended to identify the proteome components, N-terminome data is designed to identify the exact cleavage site in proteins that also evidence stable cleavage products in vivo. Here, TAILS identified 123 and 84 cleaved proteins in the AF and NP disc samples, respectively. Performing hierarchical clustering on the data we found that the two YND samples (136 and 141; Table 1) tend to cluster together in both AF and NP (Figure 7G,H; Figure 7—figure supplement 2A,B). Interestingly, the trauma sample AGD143 (53 year male), who has no known IDD diagnosis, tend to cluster with other clinically diagnosed AGD samples, in both AF and NP. This might be

Tam, Chen, et al. eLife 2020;9:e64940. DOI: https://doi.org/10.7554/eLife.64940 22 of 37 Tools and resources Computational and Systems Biology because AGD143 has unreported degeneration or ageing is a dominant factor in degradome signals. We identified two protein/peptide modules in the AF (Figure 7G), corresponding to more degradation/cleaving in YND AF (magenta) and AGD AF (blue), respectively. There are only 13 unique proteins for proteins/peptides more degraded in the YND AF, the most common of which is COL1A1/2, followed by COL2A1. In comparison, the module corresponding to more degradation in AGD AF recorded 24 unique proteins, 7 (CILP, CILP2, COL1A1, COMP, HBA1, HBB, PRELP) of which À are in strong overlap (c2 p=2.0 Â 10 71) with the 99 proteins higher in outer AF in the spatial proteome (Figure 3I). This indicates that key proteins defining a young outer AF is experiencing faster degradation in aged and degenerated samples. Similarly, we identified two modules in the NP (Figure 7H), whereby one (magenta) corresponds to more degradation in the YND, and the other (blue) corresponds to more degradation in the AGD. Only 10 unique proteins were recorded in the magenta module (for YND), with COL2A1 being the most dominant (928 peptides); whereas 32 were recorded for the blue module (for AGD). Over- all, there are more unique proteins involved in faster degradation in AGD AF and NP.

MRI landscape correlates with proteomic landscape We tested for a correlation between MRI signal intensity and proteome composition. In conventional 3T MRI of young discs, the NP is brightest reflecting its high hydration state while the AF is darker, thus less hydrated (Figure 1B; Figure 8—figure supplement 1B). Since aged discs present with more MRI phenotypes, we used higher resolution MRI (7T) on them (Figure 1C; Figure 8A), which showed less contrast between NP and AF than in the young discs. To enhance robustness, we obtained three transverse stacks per disc level for the aged discs (Figure 8B; Figure 8—figure supplement 1D), and averaged the pixel intensities for the different compartments showing that overall, the inner regions were still brighter than the outer (Figure 8C). Next, we performed a level-compartment bi-clustering on the pixel intensities of the aged disc MRIs, which was bound by disc level and compartment (Figure 8D). The findings resembled the proteomic PCA (Figure 2) and clustering (Figure 3—figure supplement 1V–W) patterns. We performed a pixel intensity averaging of the disc compartments from the 3T images (Figure 8—figure supplement 1D), and a level-compartment bi-clustering on the pixel intensities (Figure 8—figure supplement 1C). While the clustering can clearly partition the inner from the outer disc compartments, the information value from each of the compartments is less due to the lower resolution of the MRI. In all, these results indicate a link between regional MRI landscapes and proteome profiles, prompting us to investigate their potential connections.

Proteome-wide association with MRI landscapes reveals a hydration matrisome The MRI and the static proteome were done on the same specimens in both individuals, so we could perform proteome-wide associations with the MRI intensities. We detected 85 significantly correlated ECM proteins, hereby referred to as the hydration matrisome (Figure 8E). We found no collagen to be positively correlated with brighter MRI, which fits current understanding as collagens contribute to fibrosis and dehydration. Other classes of matrisome proteins were either positively or negatively correlated, with differential components for each class (Figure 8E). Positively correlated proteoglycans included EPYC, PRG4 and VCAN, consistent with their normal expression in a young disc and hydration properties. Negatively correlated proteins included OAF (TNC, SLRPs) and fibrotic (POSTN) markers (Figure 8E). Given this MRI-proteome link and the greater dynamic ranges of MRI in the aged discs enabled by the higher resolution 7T MRIs (Figure 8D), we hypothesised that the hydration matrisome might be used to provide information about MRI intensities and thus disc hydration. To test this, we trained a LASSO regression model (Tibshirani, 1996) of the aged MRIs using the hydration matrisome (85 proteins), and applied the model to predict the intensity of the MRIs of the young discs, based on the young proteome of the same 85 proteins. Remarkably, we obtained a PCC of 0.689 À (p=8.9 Â 10 6; Spearman = 0.776) between the actual and predicted MRI (Figure 8—figure supplement 1E). The predicted MRI intensities of the young disc exhibited a smooth monotonic decrease from the NP towards IAF, then dropped suddenly towards the OAF (Figure 8F, right panel), with an

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A B L3/4, Stack-2A L4/5, Stack-2A L5/S1, Stack-2 A 10 Stack 1 Stack 2 4 L R Stack 3 L 1 R L R 862 3 7 9 5 11 D P P P

NP C L3/4 L4/5 L5/S1 NP/IAF (L) 1 2 3 4 5 6 7 8 9 10 11 NP/IAF (A) NP/IAF (P) NP/IAF (R) IAF (R) IAF (L) OAF (R) OAF (L) OAF (P) OAF (A) intensities L3/4(3) L4/5(3) L4/5(2) L4/5(1) L3/4(2) L3/4(1) L5/S1(3) L5/S1(2) L5/S1(1) 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.1 0.30.20.4 0.5 0.6 NP NP NP MRI Intensities IAF L IAF R OAF L OAF R OAF A OAF P IAF L IAF R OAF L OAF R OAF A OAF P IAF L IAF R OAF L OAF R OAF A P OAF NP/IAF L NP/IAF R NP/IAF A NP/IAF P NP/IAF L NP/IAF R NP/IAF A NP/IAF P NP/IAF L NP/IAF R NP/IAF A P NP/IAF

F LASSO predicted MRI E Real MRI intensities intensities in the young discs Collagens Glycoproteins ECM regulators in the young discs (based 76 ECM genes) Proteoglycans ECM affiliated Secreted factors 0.5

0.75

0.4

0.50

0.3 ASPN ANXA4 ANXA5 ANXA1 CLEC3B CTSD CTSB SERPINB1 LOX SERPINH1 S100A9 PTN S100A11 S100A4 COL6A2 COL6A3 COL6A1 COL12A1 COL15A1 COL14A1 COL1A1 FMOD PRELP OGN HSPG2 THBS1 FBLN1 THBS4 MFGE8 MMRN2 TNXB LAMC1 NTN1 VTN TNC THBS2 NID2 FBN1 MATN2 LAMB2 VWA1 POSTN LAMA5 FGA FGB ANXA6 ANXA2 SEMA3E 0.25 0.0 0.5 0.2 FN1 A2M Real MRI intensities ISM2 CST3 MBL2 SDC2 PPBP PRG4 EPYC ECM1 VCAN CHRD TIMP1 TIMP2 TIMP3 FSTL1 LOXL3 LOXL2 INHBA TGFB1 AEBP1 HTRA3 SPARC CSPG4 HGFAC MXRA5 0.00 PDGFC IGFBP3 FAM20C Predicted MRI intensities TNFAIP6 SEMA3A MRI intensities SERPINA7 SERPINE1 SERPINA5 SERPINA3 SERPINE2 Correlation with SERPING1

í NP IAF NP IAF OAF OAF

NP/IAF NP/IAF

G INHBA THBS1 development FMOD ageing, injury DKK3 CHRDL2 DCN degeneration FRZB CHRD CD109 Shh (HHIP, SCUBE) Wnt Bmp TGFβ TGFβ (TGFBI, LTBP1) notochordal cells (KRT19, KRT8, DSC3) Hypertrophy chondrocytes (LECT1/2, CLEC3A) hypertrophy (COL10A1, IBSP) proteolytic (HTRA1,ADAMTS, MMP) S100A1 newly in"ammation synthesised stress (SODĻ, CLU) senescence (RibosomalĻ) bleeding (CD14, CD163,HBB) young #brosis (POSTN, FN1) dehydration young

proteome

aged degradome aged

H Y1 (NP) "attened young aged Y2 (AF) marginally

detected Y3 (SMC)

variable marginally set variable detected set constant set Y4 (Blood) inverted constant Cellularity

set Cellularity

hydration matrisome

young and aged or homeostatic degenerated

Figure 8. MRI intensities and their correlation with the proteomic data. (A) The middle MRI stack of each disc level in the aged cadaveric sample. (B) Schematic of the disc showing the three stacks of MRI images per disc. (C) Violin plots showing the pixel intensities within each location per disc level, corresponding to the respective locations taken for mass spectrometry measurements. Each violin-plot is the aggregate of three stacks of MRIs per disc. (D) A heatmap bi-clustering of levels and compartments based on the MRI intensities. (E) The hydration extracellular matrices (ECMs): the ECM Figure 8 continued on next page

Tam, Chen, et al. eLife 2020;9:e64940. DOI: https://doi.org/10.7554/eLife.64940 24 of 37 Tools and resources Computational and Systems Biology

Figure 8 continued proteins most positively and negatively correlated with MRI. (F) The 3T MRI intensities of the young discs across the compartments (left), and the predicted MRI intensities based on a LASSO regression model trained on the hydration ECMs (right). (G) A water-tank model of the dynamics in disc proteomics showing the balance of the proteome is maintained by adequate anabolism to balance catabolism. (H) Diagram showing the partitioning of the detected proteins into variable and constant sets, whereby four modules characterising the young healthy disc were further derived; and showing their changes with ageing. SMC: smooth muscle cell markers. The online version of this article includes the following figure supplement(s) for figure 8: Figure supplement 1. MRI-molecule connections.

ROC AUC (receiver operating characteristics, area under the curve) of 0.996 between IAF and OAF (Figure 8—figure supplement 1F). In comparison, actual MRIs exhibited a linear decrease from NP to OAF (Figure 8F, left panel). On reviewing these two patterns, we argue that the predicted intensities may be a more faithful representation of the young discs’ water contents than the actual MRI, as it reflects the gross images (Figure 1—figure supplement 1A), PCA (Figure 3A) and Y1 modular trend in the young discs (Figure 3E). This exercise not only revealed the inherent connections between regional MRI and regional proteome, but also identified a set of ECM components that is predictive of MRI relating to disc hydration, which may be valuable for future clinical applications.

Discussion Here, we present DIPPER – a human IVD proteomic resource comprising point-reference genome- wide profiles. The discovery dataset was established from intact lumbar discs of a young cadaver with no history of skeletal abnormalities (e.g. scoliosis), and an aged cadaver with reduced IVD MRI intensity and annular tears. Although these two individuals may not be representative of their respective age-groups, this is the first known attempt to achieve high spatial resolution profiles in the discs, adding a critical and much needed dimension to the current available IVD proteomic datasets. We showed that our spatiotemporal proteomes integrate well with the dynamic proteome and transcriptome of clinical samples, demonstrating their application values with other datasets. In creating the point-references, we use a well-established protein extraction protocol (O¨ nnerfjord et al., 2012), and chromatographic fractionation of the peptides prior to mass spectrometry, we produced a dataset of the human IVD comprising 3,100 proteins, encompassing ~400 matrisome and 2,700 non-matrisome proteins, with 1,769 proteins detected in three or more profiles, considerably higher than recent studies (Maseda et al., 2016; Rajasekaran et al., 2020; Ranjani et al., 2016; Sarath Babu et al., 2016). The high quality of our data enabled the application of unbiased approaches including PCA and ANOVA to reveal the relative importance of the phenotypic factors. Particularly, age was found to be the dominant factor influencing proteome profiles. Comparisons between different compartments of the young disc produced a reference landscape containing known (KRT8/19 for the NP; COL1A1, CILP and COMP for the AF) and novel (FRZB, CHRD, CHRDL2 for the NP; TNMD, SLRPs, and SOD1 for the AF) markers. The young healthy discs were enriched for matrisome components consistent with a healthy functional young IVD. Despite morphological differences between NP and IAF, the inner disc compartments (NP, NP/IAF, and IAF) display high similarities, in contrast to the large differences between IAF and OAF, which was consistent for discs from all lumbar disc levels. This morphological-molecular discrepancy might be accounted for by subtle differences in the ECM organisation, such as differences in GAG moieties on proteoglycans, or levels of glycosylation or other modifications of ECM that diversify function (Silagi et al., 2018). Nonetheless, we partitioned the detected proteins into a variable set that captures the diversity, and a constant set that lays the common foundation of all young compartments, which work in synergy to achieve disc function. Clustering analysis of the 671 DEPs of the variable set identified four key modules (Y1-Y4). Visu- ally, Y1 and Y2 mapped across the lateral and anteroposterior axes with opposing trends. Molecu- larly, module Y1 (NP) contained proteins promoting regulation of matrix remodelling, such as matrix degradation inhibitor MATN3 (Jayasuriya et al., 2012) and MMP inhibitor TIMP1. Inhibitors of WNT and BMP signalling were also present. Module Y2 (OAF) included COL1A1, THBS1/2/3, CILP1/ CILP2, and TNMD, consistent with the OAF’s tendon-like features (Nakamichi et al., 2018). It also included a set of SLRPs that might play roles in regulation of collagen assembly (Robinson et al.,

Tam, Chen, et al. eLife 2020;9:e64940. DOI: https://doi.org/10.7554/eLife.64940 25 of 37 Tools and resources Computational and Systems Biology 2017; Taye et al., 2020), fibril alignment (Robinson et al., 2017), maturation and crosslinking (Kalamajski et al., 2016), while others are known to inhibit or promote TGFb signalling (Markmann et al., 2000). Notably, the composition of the IAF appeared to be a transition zone between NP and OAF rather than an independent compartment, as few proteins can distinguish it from adjacent compartments. Classes of proteins in both Y3 (smooth muscle feature) and Y4 (immune and blood) resemble Y2, which reflects the contractile property of the AF (Nakai et al., 2016) and the capillaries infiltrating or present at the superficial outer surface of the IVD. In the aged disc, the DEPs between the inner and outer regions of the discs suggests extensive changes in the inner compartment(s). Mapping the aged data onto modules Y1-Y4 allowed a visuali- sation of the changes. The flattening of the Y1 and Y2 modules along both the lateral and anteroposterior axes indicated a convergence of the inner and outer disc. This is supported by the observed rapid decline of NP proteins and increase of AF proteins in the inner region. Fewer changes were seen in the aged OAF, which concurs with the notion that degenerative changes origi- nate from the NP and radiate outwards; however, infringement of IAF into the NP cannot be excluded. The most marked change was seen in module Y4 (blood), where the pattern was inverted, characterised by high expression in the NP but low in OAF. While contamination cannot be excluded and there are reports that capillaries do not infiltrate the NP even in degeneration (Nerlich et al., 2007), our finding is consistent with other proteomic studies showing enrichment of blood proteins in pathological NP (Maseda et al., 2016). The route of infiltration can be from the fissured AF or cartilage endplates. Calcified endplates are more susceptible to microfractures, which can lead to blood infiltration into the NP (Sun et al., 2020). Of interest is the involvement of an immune response within the inner disc. This corroborates reports of inflammatory processes in ageing and degenerative discs, with the upregulation of pro-inflammatory cytokines and presence of inflammatory cells (Molinos et al., 2015; Wuertz et al., 2012). The SILAC and degradome studies provided important insights into age-related differences in the biosynthetic and turnover activity in the IVD. The SILAC data indicated that protein synthesis is significantly impaired in aged degenerated discs. These findings correlate with reports of reduced cellularity in ageing (Rodriguez et al., 2011), which we have also ascertained by leveraging the relationship of histones and housekeeping genes with cell numbers. From the TAILS degradome analysis, we observed more cleaved protein fragments in aged compartments, particularly for structural proteins important for tissue integrity such as COMP and those involved in cell-matrix interactions such as FN1, which was coupled with the enrichment of the proteolytic process GO terms in the aged static proteome. Collectively, this reveals a systematic modification and replacement of the pri- mary proteomic architecture of the young IVD with age that is associated with diminished or failure in functional properties in ageing or degeneration. Despite known transcriptome-proteome discordance (Fortelny et al., 2017), our identification of their concordance allows insights into active changes in the young and aged discs. For example, inhibitors of the WNT pathway and antagonists of BMP/TGFb signalling (Leijten et al., 2013) were down-regulated in the aged discs in both the proteome and transcriptome. Interestingly, the activation of these pathways is known to promote chondrocyte hypertrophy (Dong et al., 2006), and hypertrophy has been noted in IDD (Rutges et al., 2010). This suggests a model whereby the regulatory environment suppressing cellular hypertrophy changes with ageing or degeneration, resulting in conditions such as cellular senescence and tissue mineralisation that are part of the pathological process. In support, S100A1, a known inhibitor of chondrocyte hypertrophy (Saito et al., 2007) is downregulated, while chondrocyte hypertrophy markers COL10A1 and IBSP (Komori, 2010) are upregulated in the aged disc. Similar changes have been observed in ageing mouse NP (Veras et al., 2020) as well as in osteoarthritis (Zhu et al., 2009) where chondrocyte hypertrophy is thought to be involved in its aetiology (Ji et al., 2019; van der Kraan and van den Berg, 2012). Given that WNT inhibitors are already in clinical trials for osteoarthritis (Wang et al., 2019b), this may point to a prospective therapeutic strategy for IDD. A key finding of our study is the direct demonstration, within a single individual, of association between the hydration status of the disc as revealed by MRI, and the matrisome composition of the disc proteome. The remarkable correlation between predicted hydration states inferred from the spatial proteomic data and the high-definition phenotyping of the aged disc afforded by 7T MRI has enormous potential for understanding the molecular processes underlying IDD.

Tam, Chen, et al. eLife 2020;9:e64940. DOI: https://doi.org/10.7554/eLife.64940 26 of 37 Tools and resources Computational and Systems Biology In conclusion, we have generated point-reference datasets of the young and aged disc proteome, at a significantly higher spatial resolution than previous works. By means of a methodological frame- work, we revealed compartmentalised information on the ECM composition and cellular activities, and their changes with ageing. Integration of this point-reference with additional age- and protein- specific information of synthesis/degradation helps gain insights into the underlying molecular pathology of degeneration (Figure 8G and H). The richness of information in DIPPER makes it a valuable resource for cross referencing with human, animal and in vitro studies to evaluate clinical relevance and guide the development of therapeutics for human IDD.

Materials and methods Cadaveric specimens Two human lumbar spines were obtained through approved regulations and governing bodies, with one young (16M) provided by L.H. (McGill University) and one aged (59M) from Articular Engineer- ing, LLC (IL, USA). The young lumbar spine was received frozen as an intact whole lumbar spine. The aged lumbar spine was received frozen, dissected into bone-disc-bone segments. The cadaveric samples were stored at À800C until use.

Clinical specimens Clinical specimens were obtained with approval by the Institutional Review Board (references UW 13–576 and EC 1516–00 11/01/2001) and with informed consent in accordance with the Helsinki Declaration of 1975 (revision 1983) from another 15 patients undergoing surgery for IDD, trauma or adolescent idiopathic scoliosis at Queen Mary Hospital (Hong Kong), and Duchess of Kent Children’s Hospital (Hong Kong). Information of both the cadaveric and clinical samples are summarised in Table 1.

MRI imaging of cadaveric samples The discs were thawed overnight at 4˚C, and then pre-equalised for scanning at room temperature. For the young IVD, these were imaged together as the lumbar spine was kept intact. T2-weighted and T1-weighted sagittal and axial MRI, T1-rho MRI and Ultrashort-time-to-echo MRI images were obtained using a 3T Philips Achieva 3.0 system at the Department of Diagnostic Radiology, The Uni- versity of Hong Kong. For the aged discs, the IVD were imaged separately as bone-disc-bone segments, at the Depart- ment of Electrical and Electronic Engineering, The University of Hong Kong. The MRS and CEST imaging were performed. The FOV for the CEST imaging was adjusted to 76.8 Â 76.8 mm2 to accommodate the size of human lumbar discs (matrix size = 64 Â 64, slice thickness = 2 mm). All MRI experiments were performed at room temperature using a 7 T pre-clinical scanner (70/16 Phar- mascan, Bruker BioSpin GmbH, Germany) equipped with a 370 mT/m gradient system along each axis. Single-channel volume RF coils with different diameters were used for the samples based on size (60 mm for GAG phantoms and human cadaveric discs).

Image assessment of the aged lumbar IVD The MRI images in the transverse view were then assessed for intensity of the image (brighter signi- fying more water content). Three transverse MRI images per IVD were overlaid with a grid representing the areas that were cut for mass-spectrometry measurements as outlined previously. For each region, the ‘intensity’ was represented by the average of the pixel intensities, which were graphically visualised and used for correlative studies.

Division of cadaveric IVD for mass spectrometry analysis The endplates were carefully cut off with a scalpel, exposing the surface of the IVD, which were then cut into small segments spanning seven segments in the central left-right lateral axis, and five segments in the central anteroposterior axis (Figure 1C). In all, this corresponds to a total of 11 locations per IVD. Among them, four are from the OAF, two from the IAF (but only in the lateral axis), one from the central NP, and four from a transition zone between IAF and the NP (designated the ‘NP/IAF’). Samples were stored frozen at À800C until use.

Tam, Chen, et al. eLife 2020;9:e64940. DOI: https://doi.org/10.7554/eLife.64940 27 of 37 Tools and resources Computational and Systems Biology SILAC by ex vivo culture of disc tissues NP and AF disc tissues from spine surgeries (Table 1) were cultured in custom-made Arg- and Lys- free a-MEM (AthenaES) as per formulation of Gibco a-MEM (Cat #11900–024), supplemented with 10% dialysed FBS (10,000 MWCO, Biowest, Cat# S181D), penicillin/streptomycin, 2.2 g/L sodium 13 bicarbonate (Sigma), 30 mg/L L-methionine (Sigma), 21 mg/L ‘heavy’ isotope-labelled C6L-arginine (Arg6, Cambridge Isotopes, Cat # CLM-2265-H), 146 mg/L ‘heavy’ isotope-labelled 4,4,5,5-D4 L-Lysine (Lys4, Cambridge Isotopes, Cat # DLM-2640). Tissue explants were cultured for 7 days in 0 hypoxia (1% O2 and 5% CO2 in air) at 37 C before being washed with PBS and frozen until use. Protein extraction and preparation for cadaveric and SILAC samples The frozen samples were pulverised using a freezer mill (Spex) under liquid nitrogen. Samples were extracted using 15 volumes (w/v) of extraction buffer (4M guanidine hydrochloride (GuHCl), 50 mM sodium acetate, 100 mM 6-aminocaproic acid, and HALT protease inhibitor cocktail (Thermo Fischer Scientific), pH 5.8). Samples were mechanically dissociated with 10 freeze-thaw cycles and sonicated in a cold water bath, before extraction with gentle agitation at 4˚C for 48 hr. Samples were centrifuged at 15,000 g for 30 min at 4˚C and the supernatant was ethanol precipitated at a ratio of 1:9 for 16 hr at À20˚C. The ethanol step was repeated and samples were centrifuged at 5000 g for 45 min at 4˚C, and the protein pellets were air dried for 30 min. Protein pellets were re-suspended in fresh 4M urea in 50 mM ammonium bicarbonate, pH 8, using water bath sonication to aid in the re-solubilisation of the samples. Samples underwent reduction with TCEP (5 mM final concentration) at 60˚C for 1 hr, and alkylation with iodoacetamide (500 mM final concentration) for 20 min at RT. Protein concentration was measured using the BCA assay (Biorad) according to manufacturer’s instructions. A total of 200 mg of protein was then buffer exchanged with 50 mM ammonium bicarbonate with centricon filters (Millipore, 30 kDa cutoff) according to manufacturer’s instructions. Samples were digested with mass spec grade Trypsin/LysC (Promega) as per manufacturer’s instructions. For SILAC-labelled samples, formic acid was added to a final concentration of 1%, and centrifuged and the supernatant then desalted prior to LC-MS/MS measurements. For the cadaveric samples, the digested peptides were then acidified with TFA (0.1% final concentration) and quantified using the peptide quantitative colorimetric peptide assay kit (Pierce, catalogue 23275) before undergoing fractionation using the High pH reversed phase peptide fractionation kit (Pierce, catalogue number 84868) into four fractions. Desalted peptides were dried, re-suspended in 0.1% formic acid prior to LC-MS/MS measurements.

Mass spectrometry for cadaveric and SILAC samples Samples were loaded onto the Dionex UltiMate 3000 RSLC nano Liquid Chromatography coupled to the Orbitrap Fusion Lumos Tribid Mass Spectrometer. Peptides were separated on a commercial Acclaim C18 column (75 mm internal diameter Â50 cm length, 1.9 mm particle size; Thermo). Separa- tion was attained using a linear gradient of increasing buffer B (80% ACN and 0.1% formic acid) and declining buffer A (0.1% formic acid) at 300 nL/min. Buffer B was increased to 30% B in 210 min and ramped to 40% B in 10 min followed by a quick ramp to 95% B, where it was held for 5 min before a quick ramp back to 5% B, where it was held and the column was re-equilibrated. Mass spectrometer was operated in positive polarity mode with capillary temperature of 300˚C. Full survey scan resolution was set to 120,000 with an automatic gain control (AGC) target value of 2 Â 106, maximum ion injection time of 30 ms, and for a scan range of 400–1500 m/z. Data acquisition was in DDA mode to automatically isolate and fragment topN multiply charged precursors according to their intensities. Spectra were obtained at 30,000 MS2 resolution with AGC target of 1 Â 105 and maximum ion injection time of 100 ms, 1.6 m/z isolation width, and normalised collisional energy of 31. Preceding precursor ions targeted for HCD were dynamically excluded of 50 s.

Label-free quantitative data processing for cadaveric samples Raw data were analysed using MaxQuant (v.1.6.3.3, Germany). Briefly, raw files were searched using Andromeda search engine against human UniProt protein database (20,395 entries, Oct 2018), supplemented with sequences of contaminant proteins. Andromeda search parameters for protein identification were set to a tolerance of 6 ppm for the parental peptide, and 20 ppm for fragmentation spectra and trypsin specificity allowing up to two miscleaved sites. Oxidation of methionine,

Tam, Chen, et al. eLife 2020;9:e64940. DOI: https://doi.org/10.7554/eLife.64940 28 of 37 Tools and resources Computational and Systems Biology carboxyamidomethylation of cysteines was specified as a fixed modification. Minimal required peptide length was specified at seven amino acids. Peptides and proteins detected by at least two LFQ ion counts for each peptide in one of the samples were accepted, with a false discovery rate (FDR) of 1%. Proteins were quantified by normalised summed peptide intensities computed in MaxQuant with the LFQ option enabled. A total of 66 profiles were obtained: 11 locations Â 3 disc levels Â 2 individuals; with a median of 665 proteins (minimum 419, maximum 1920) per profile.

Data processing for SILAC samples The high-resolution, high mass accuracy mass spectrometry (MS) data obtained were processed using Proteome Discoverer (Ver 2.1), wherein data were searched using Sequest algorithm against Human UniProt database (29,900 entries, May 2016), supplemented with sequences of contaminant proteins, using the following search parameters settings: oxidised methionine (M), acetylation (Pro- tein N-term), heavy Arginine (R6) and Lysine (K4) were selected as dynamic modifications, carboxyamidomethylation of cysteines was specified as a fixed modification, minimum peptide length of 7 amino acids was enabled, tolerance of 10 ppm for the parental peptide, and 20 ppm for fragmentation spectra, and trypsin specificity allowing up to two miscleaved sites. Confident proteins were identified using a target-decoy approach with a reversed database, strict FDR 1% at peptide and PSM level. Newly synthesised proteins were heavy labelled with Arg6- and Lys4, and the data was expressed as the normalised protein abundance obtained from heavy (labelled)/light (un-labelled) ratio.

Degradome sample preparation, mass spectrometry, and data processing Degradome analyses was performed on NP and AF from three non-degenerated and three degenerated individuals (Table 1). Frozen tissues were pulverised as described above and prepared for TAILS as previously reported (Kleifeld et al., 2010). After extraction with SDS buffer (1% SDS, 100 mM dithiothreitol, 1X protease inhibitor in deionised water) and sonication (three cycles, 15 s/cycle), the supernatant (soluble fraction) underwent reduction at 37˚C and alkylation with a final concentration of 15 mM iodoacetamide for 30 min at RT. Samples were precipitated using chloroform/methanol, and the protein pellet air dried. Samples were re-suspended in 1M NaOH, quantified by nanodrop, diluted to 100 mM HEPES and 4M GnHCl and pH adjusted (pH6.5–7.5) prior to 6-plex TMT labelling as per manufacturer’s instructions (Sixplex TMT, Cat# 90061, ThermoFisher Scientific). Equal ratios of TMT-labelled samples were pooled and methanol/chloroform precipitated. Protein pellets were air-dried and re-suspended in 200 mM HEPES (pH8), and digested with trypsin (1:100 ratio) for 16 hr at 37˚C, pH 6.5 and a sample was taken for pre-TAILS. High-molecular-weight den-

dritic polyglycerol aldehyde polymer (ratio of 5:1 w/w polymer to sample) and NaBH3CN (to a final concentration of 80 mM) was added, incubated at 37˚C for 16 hr, followed by quenching with 100 mM ethanolamine (30 min at 37˚C) and underwent ultrafiltration (MWCO of 10,000). Collected samples were desalted, acidified to 0.1% formic acid and dried, prior to MS analysis. Samples were analysed on a Thermo Scientific Easy nLC-1000 coupled online to a Bruker Dalton- ics Impact II UHR QTOF. Briefly, peptides were loaded onto a 20 cm x 75 mm I.D. analytical column

packed with 1.8 mm C18 material (Dr. Maisch GmbH, Germany) in 100% buffer A (99.9% H2O, 0.1% formic acid) at 800 bar followed by a linear gradient elution in buffer B (99.9% acetonitrile, 0.1% formic acid) to a final 30% buffer B for a total 180 min including washing with 95% buffer B. Eluted peptides were ionised by ESI and peptide ions were subjected to tandem MS analysis using a data- dependent acquisition method. A top17 method was employed, where the top 17 most intense multiply charged precursor ions were isolated for MS/MS using collision-induced-dissociation, and actively excluded for 30 s. MGF files were extracted and searched using Mascot against the UniProt Homo sapiens database, with semi-ArgC specificity, TMT6plex quantification, variable oxidation of methionine, variable acetylation of N termini, 20 ppm MS1 error tolerance, 0.05 Da MS2 error tolerance and two missed cleavages. Mascot. dat files were imported into Scaffold Q+S v4.4.3 for peptide identification processing to a final FDR of 1%. Quantitative values were calculated through Scaffold and used for sub- sequent analyses.

Tam, Chen, et al. eLife 2020;9:e64940. DOI: https://doi.org/10.7554/eLife.64940 29 of 37 Tools and resources Computational and Systems Biology Transcriptomic samples: isolation, RNA extraction, and data processing AF and NP tissues from four individuals were cut into approximately 0.5 cm3 pieces, and put into the Dulbecco’s modified Eagle’s medium (DMEM) (Gibco) supplemented with 20 mM HEPES (USB), 1% penicillin-streptomycin (Gibco) and 0.4% fungizone (Gibco). The tissues were digested with 0.2% pronase (Roche) for 1 hr, and centrifuged at 200 g for 5 min to remove supernatant. AF and NP were then digested by 0.1% type II collagenase (Worthington Biochemical) for 14 hr and 0.05% type II collagenase for 8 hr, respectively. Cell suspension was filtered through a 70 mm cell strainer (BD Falcon) and centrifuged at 200 g for 5 min. The cell pellet was washed with phosphate buffered saline (PBS) and centrifuged again to remove the supernatant. RNA was then extracted from the isolated disc cells using Absolutely RNA Nanoprep Kit (Stratagene), following manufacturer’s protocol, and stored at À80˚C until further processing. The quality and quantity of total RNA were assessed on the Bioanalyzer (Agilent) using the RNA 6000 Nano total RNA assay. cDNA was generated using Affymetrix GeneChip Two-Cycle cDNA Syn- thesis Kit, followed by in vitro transcription to produce biotin-labelled cRNA. The sample was then hybridised onto the Affymetrix GeneChip Human Genome U133 Plus 2.0 Array. The array image, CEL file and other related files were generated using Affymetrix GeneChip Command Console. The experiment was conducted as a service at the Centre for PanorOmic Sciences of the University of Hong Kong. CEL and other files were loaded into GeneSpring GX 10 (Agilent) software. The RMA algorithm was used for probe summation. Data were normalised with baseline transformed to median of all samples. A loose filtering based on the raw intensity values was then applied to remove background noise. Consequently, transcriptomic data with a total of 54,675 probes (corresponding to 20,887 genes) and eight profiles were obtained.

Bioinformatics and functional analyses The detected proteins were compared against the transcription factor (TF) database (Vaquerizas et al., 2009) and the human genome nomenclature consortium database for cell surface markers (CDs) (Braschi et al., 2019), where 77 TFs and 83 CDs were detected (Figure 1—figure supplement 3A). Excluding missing values, the LFQ levels among the data-points range from 15.6 to 41.1, with a Gaussian-like empirical distribution (Figure 2—figure supplement 1A). The numbers of valid values per protein were found to decline rapidly when they were sorted in descending order (Figure 2—figure supplement 1B, upper panel). To perform PCAs, only a subset of genes with suffi- ciently large numbers of valid values (i.e. non-missing values) were used. The cutoff for this was cho- sen based on a point corresponding to the steepest slope of descending order of valid protein numbers (Figure 2—figure supplement 1B, second panel), such that the increase of valid values is slower than the increase of missing values beyond that point. Subsequently, the top 507 genes were picked representing 59.8% of all valid values. This new subset includes 12.4% of all missing values. Since the subset of data still contains some missing values, an imputation strategy was adopted employing the Multiple Imputation by Chained Equations (MICE) method and package (Buuren and Groothuis-Oudshoorn, 2011), with a max iteration set at 50 and the default PMM method (predictive mean matching). To further ensure normality, Winsorisation was applied such that genes whose average is below 5% or above 95% of all genes were also excluded from PCA. The data was then profile-wise standardised (zero-mean and one standard deviation) before PCA was applied on the R platform (R Development Core Team, 2013). To assess the impact of the spatiotemporal factors on the proteomic profiles, we performed Anal- ysis of Variance (ANOVA), correlating each protein to the age, compartments, level, and directional- ity. To draw the soft boundaries on the PCA plot between groups of samples, support vector machines with polynomial (degree of 2) kernel were applied using the LIBSVM package (Chang and Lin, 2011) and the PCA coordinates as inputs for training. A meshed grid covering the whole PCA field was created to make prediction and draw probability contours for À0.5, 0, and 0.5 from the fitted model. Hierarchical clustering was performed with (1- correlation coefficient) as the distance metrics unless otherwise specified. To address the problem of ‘dropout’ effects while avoiding extra inter-dependency introduced due to imputations, we adopted three strategies in calculating the DEPs, namely, by statistical testing, exclusively detected, and fold-change cutoff approaches. First, for the proteins that have over

Tam, Chen, et al. eLife 2020;9:e64940. DOI: https://doi.org/10.7554/eLife.64940 30 of 37 Tools and resources Computational and Systems Biology half valid values in both groups under comparison, we performed t-testing with p-values adjusted for multiple testing by the false discovery rate (FDR). Those with FDR below 0.05 were considered statistical DEPs. Second, for the proteins where one group has some valid values while the other group is completely not detected, we considered the ones with over half valid values in one group to be exclusive DEPs. For those proteins that were expressed in <50% in both groups, the ones with fold-change greater than two were also considered to be DEPs. To fit the lateral and anteroposterior trends for the modules of genes identified in the young samples, a Gaussian Process Estimation (GPE) model was trained using the GauPro package in R Development Core Team, 2013. Pathway analyses was conducted on the GSEA (Subramanian et al., 2005). Signalling proteins was compiled based on 25 Signal transduction pathways listed on KEGG (Kanehisa et al., 2019). For transcriptomic data, we used a thresholding approach to detect DEGs (differentially

expressed genes), whereby a gene was considered a DEG if the log2(fold-change) is greater than three and the average expression (logarithmic scale) is greater than 10 (Figure 6—figure supplement 1E–H). The LASSO model between MRI and proteome was trained using the R package ‘glmnet’, wherein the 85 ECMs were first imputed for missing values in them using MICE. Nine ECMs were not imputed for too many missing values, leaving 76 for training and testing. The best value for l was determined by cross-validations. A model was then trained on the aged MRIs (dependent variable) and aged proteome of the 76 genes (independent variable). The fitted model was then applied to the young proteome to predict MRIs of the young discs.

Software availability The custom scripts for processing and analysing the data were housed at github.com/hkudclab/DIP- PER; Tam, 2021; copy archived at swh:1:rev:49e4da79786de1dfa496d7c7472343f3b865696c. An interactive web interface for the data is available at http://www.sbms.hku.hk/dclab/DIPPER/.

Acknowledgements We thank Dr Dino Samartzis for arranging the MRI of the young lumbar spine, and Prof. Kenneth Cheung and Dr Jason Cheung for collecting surgical disc specimens. We thank Dr Ed Wu and Dr Anna Wang of the Department of Electrical and Electronic Engineering at HKU for performing the high-resolution MRI on the aged discs. Part of this work was supported by the Theme-based Research Scheme (T12-708/12N) and Area of Excellence (AoE/M-04/04) of the Hong Kong Research Grants Council (RGC) (Kathryn Cheah, Danny Chan), the RGC European Union - Hong Kong Research and Innovation Cooperation Co-funding Mechanism (iPSpine) (E-HKU703/18) (Danny Chan), and by the Ministry of Science and Technology of the People’s Republic of China: National Strategic Basic Research Program (‘973’) (2014CB942900) (Danny Chan). The TAILS analyses were supported by a Canadian Institutes of Health Research210312 Foundation Grant (FDN-148408) (Christopher Overall).

Additional information

Competing interests Kathryn Song Eng Cheah: Senior editor, eLife. Theo Klein: Theo Klein is affiliated with Triskelion BV. The author has no financial interests to declare. The other authors declare that no competing interests exist.

Funding Funder Grant reference number Author Research Grants Council, Uni- E-HKU703/18 Danny Chan versity Grants Committee Research Grants Council, Uni- T12-708/12N Kathryn Song Eng Cheah versity Grants Committee

Tam, Chen, et al. eLife 2020;9:e64940. DOI: https://doi.org/10.7554/eLife.64940 31 of 37 Tools and resources Computational and Systems Biology

Research Grants Council, Uni- AoE/M-04/04 Kathryn Song Eng Cheah versity Grants Committee Ministry of Science and Tech- ("973") (2014CB942900) Danny Chan nology of the People’s Repub- lic of China Canadian Institutes of Health FDN-148408 Christopher M Overall Research

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Author contributions Vivian Tam, Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing; Peikai Chen, Conceptualization, Data curation, Software, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing; Anita Yee, Data curation, Formal analysis, Methodology, Writing - review and editing; Nestor Solis, Data curation, Methodology, Writing - review and editing; Theo Klein, Rakesh Sharma, Data curation, Writing - review and editing; Mateusz Kudelko, Data curation, Formal analysis, Writing - review and editing; Wilson CW Chan, Formal analysis, Writing - review and editing; Christopher M Overall, Conceptualization, Supervision, Writing - review and editing; Lisbet Haglund, Conceptualization, Resources, Writing - review and editing; Pak C Sham, Resources, Meth- odology, Writing - review and editing; Kathryn Song Eng Cheah, Conceptualization, Resources, Supervision, Funding acquisition, Writing - review and editing; Danny Chan, Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

Author ORCIDs Vivian Tam https://orcid.org/0000-0002-0457-3477 Peikai Chen https://orcid.org/0000-0003-1880-0893 Theo Klein http://orcid.org/0000-0001-8061-9353 Lisbet Haglund http://orcid.org/0000-0002-1288-2149 Kathryn Song Eng Cheah http://orcid.org/0000-0003-0802-8799 Danny Chan https://orcid.org/0000-0003-3824-5778

Ethics Human subjects: Clinical specimens were obtained with approval by the Institutional Review Board (references UW13-576 and EC 1516-00 11/01/2001) and with informed consent in accordance with the Helsinki Declaration of 1975 (revision 1983).

Decision letter and Author response Decision letter https://doi.org/10.7554/eLife.64940.sa1 Author response https://doi.org/10.7554/eLife.64940.sa2

Additional files Supplementary files . Supplementary file 1. Processed data of the 66 LC-MS/MS static spatial proteome profiles. . Supplementary file 2. Differentially expressed proteins (DEPs) among pairs of sample groups within the 33 young static spatial disc profiles. . Supplementary file 3. Differentially expressed proteins (DEPs) among pairs of sample groups within the 33 aged static spatial disc profiles. . Supplementary file 4. Differentially expressed proteins (DEPs) between young and aged sample groups of static spatial proteomes.

Tam, Chen, et al. eLife 2020;9:e64940. DOI: https://doi.org/10.7554/eLife.64940 32 of 37 Tools and resources Computational and Systems Biology . Supplementary file 5. Significantly enriched gene ontology (GO) terms associated with proteins expressed higher in all young or all aged discs. . Transparent reporting form

Data availability The mass spectrometry proteomics raw data have been deposited to the ProteomeXchange Consor- tium via the PRIDE repository with the following dataset identifiers for cadaver samples (PXD017740), SILAC samples (PXD018193), and degradome samples (PXD018298). The RAW data for the transcriptome data has been deposited on NCBI GEO with accession number GSE147383.

The following datasets were generated:

Database and Identifier Author(s) Year Dataset title Dataset URL Tam V, Chan D 2020 The degradome of the human http://www.ebi.ac.uk/ PRIDE, PXD018298 intervertebral disc pride/archive/projects/ PXD018298 Tam V, Chan D 2020 A proteomic architectural http://www.ebi.ac.uk/ PRIDE, PXD017740 landscape of the healthy and pride/archive/projects/ aging human intervertebral PXD017740 disc Tam V, Chan D 2020 Actively synthesised proteins http://www.ebi.ac.uk/ PRIDE, PXD018193 in human intervertebral disc pride/archive/projects/ PXD018193 Yee A, Tam V, 2020 Gene expression data for https://www.ncbi.nlm. NCBI Gene Expression Chen P, Chan D human intervertebral discs nih.gov/geo/query/acc. Omnibus, GSE147383 cgi?acc=GSE147383

References Anderson RM, Lawrence AR, Stottmann RW, Bachiller D, Klingensmith J. 2002. Chordin and noggin promote organizing centers of forebrain development in the mouse. Development 129:4975–4987. PMID: 12397106 Barber RD, Harmer DW, Coleman RA, Clark BJ. 2005. GAPDH as a housekeeping gene: analysis of GAPDH mRNA expression in a panel of 72 human tissues. Physiological Genomics 21:389–395. DOI: https://doi.org/10. 1152/physiolgenomics.00025.2005, PMID: 15769908 Bizet AA, Liu K, Tran-Khanh N, Saksena A, Vorstenbosch J, Finnson KW, Buschmann MD, Philip A. 2011. The TGF-b co-receptor, CD109, promotes internalization and degradation of TGF-b receptors. Biochimica Et Biophysica Acta (BBA) - Molecular Cell Research 1813:742–753. DOI: https://doi.org/10.1016/j.bbamcr.2011. 01.028 Braschi B, Denny P, Gray K, Jones T, Seal R, Tweedie S, Yates B, Bruford E. 2019. Genenames.org: the HGNC and VGNC resources in 2019. Nucleic Acids Research 47:D786–D792. DOI: https://doi.org/10.1093/nar/ gky930, PMID: 30304474 Buuren S, Groothuis-Oudshoorn K. 2011. Mice: multivariate imputation by chained equations in R. Journal of Statistical Software 45:i03. DOI: https://doi.org/10.18637/jss.v045.i03 Caldeira J, Santa C, Oso´rio H, Molinos M, Manadas B, Gonc¸alves R, Barbosa M. 2017. Matrisome profiling during intervertebral disc development and ageing. Scientific Reports 7:11629. DOI: https://doi.org/10.1038/ s41598-017-11960-0, PMID: 28912585 Chang CC, Lin CJ. 2011. LIBSVM: a library for support vector machines. Acm T Intel Syst Tec 2:1961199. DOI: https://doi.org/10.1145/1961189.1961199 Chen J, Yan W, Setton LA. 2006. Molecular phenotypes of notochordal cells purified from immature nucleus pulposus. European Spine Journal 15:303–311. DOI: https://doi.org/10.1007/s00586-006-0088-x Dong YF, Soung doY, Schwarz EM, O’Keefe RJ, Drissi H. 2006. Wnt induction of chondrocyte hypertrophy through the Runx2 transcription factor. Journal of Cellular Physiology 208:77–86. DOI: https://doi.org/10.1002/ jcp.20656, PMID: 16575901 Erwin WM, DeSouza L, Funabashi M, Kawchuk G, Karim MZ, Kim S, Madler S, Matta A, Wang X, Mehrkens KA. 2015. The biological basis of degenerative disc disease: proteomic and biomechanical analysis of the canine intervertebral disc. Arthritis Research & Therapy 17:240. DOI: https://doi.org/10.1186/s13075-015-0733-z, PMID: 26341258 Feng H, Danfelter M, Stro¨ mqvist B, Heinega˚ rd D. 2006. Extracellular matrix in disc degeneration. The Journal of Bone and Joint Surgery. American Volume 88:25–29. DOI: https://doi.org/10.2106/JBJS.E.01341, PMID: 165 95439 Fortelny N, Overall CM, Pavlidis P, Freue GVC. 2017. Can we predict protein from mRNA levels? Nature 547: E19–E20. DOI: https://doi.org/10.1038/nature22293, PMID: 28748932

Tam, Chen, et al. eLife 2020;9:e64940. DOI: https://doi.org/10.7554/eLife.64940 33 of 37 Tools and resources Computational and Systems Biology

Fujita N, Miyamoto T, Imai J, Hosogane N, Suzuki T, Yagi M, Morita K, Ninomiya K, Miyamoto K, Takaishi H, Matsumoto M, Morioka H, Yabe H, Chiba K, Watanabe S, Toyama Y, Suda T. 2005. CD24 is expressed specifically in the nucleus pulposus of intervertebral discs. Biochemical and Biophysical Research Communications 338:1890–1896. DOI: https://doi.org/10.1016/j.bbrc.2005.10.166, PMID: 16288985 Grimsrud CD, Romano PR, D’Souza M, Puzas JE, Schwarz EM, Reynolds PR, Roiser RN, O’Keefe RJ. 2001. BMP signaling stimulates chondrocyte maturation and the expression of indian hedgehog. Journal of Orthopaedic Research 19:18–25. DOI: https://doi.org/10.1016/S0736-0266(00)00017-6, PMID: 11332615 Hiyama A, Sakai D, Risbud MV, Tanaka M, Arai F, Abe K, Mochida J. 2010. Enhancement of intervertebral disc cell senescence by WNT/b-catenin signaling-induced matrix metalloproteinase expression. Arthritis & Rheumatism 62:3036–3047. DOI: https://doi.org/10.1002/art.27599, PMID: 20533544 Hou G, Lu H, Chen M, Yao H, Zhao H. 2014. Oxidative stress participates in age-related changes in rat lumbar intervertebral discs. Archives of Gerontology and Geriatrics 59:665–669. DOI: https://doi.org/10.1016/j. archger.2014.07.002, PMID: 25081833 Humzah MD, Soames RW. 1988. Human intervertebral disc: structure and function. The Anatomical Record 220: 337–356. DOI: https://doi.org/10.1002/ar.1092200402, PMID: 3289416 Jayasuriya CT, Goldring MB, Terek R, Chen Q. 2012. Matrilin-3 induction of IL-1 receptor antagonist is required for up-regulating collagen II and aggrecan and down-regulating ADAMTS-5 gene expression. Arthritis Research & Therapy 14:R197. DOI: https://doi.org/10.1186/ar4033, PMID: 22967398 Ji Q, Zheng Y, Zhang G, Hu Y, Fan X, Hou Y, Wen L, Li L, Xu Y, Wang Y, Tang F. 2019. Single-cell RNA-seq analysis reveals the progression of human osteoarthritis. Annals of the Rheumatic Diseases 78:100–110. DOI: https://doi.org/10.1136/annrheumdis-2017-212863, PMID: 30026257 Jim JJ, Noponen-Hietala N, Cheung KM, Ott J, Karppinen J, Sahraravand A, Luk KD, Yip SP, Sham PC, Song YQ, Leong JC, Cheah KS, Ala-Kokko L, Chan D. 2005. The TRP2 allele of COL9A2 is an Age-Dependent risk factor for the development and severity of intervertebral disc degeneration. Spine 30:2735–2742. DOI: https://doi. org/10.1097/01.brs.0000190828.85331.ef, PMID: 16371896 Johnson JL, Hall TE, Dyson JM, Sonntag C, Ayers K, Berger S, Gautier P, Mitchell C, Hollway GE, Currie PD. 2012. Scube activity is necessary for hedgehog signal transduction in vivo. Developmental Biology 368:193– 202. DOI: https://doi.org/10.1016/j.ydbio.2012.05.007, PMID: 22609552 Kalamajski S, Bihan D, Bonna A, Rubin K, Farndale RW. 2016. Fibromodulin interacts with collagen Cross-linking sites and activates lysyl oxidase. Journal of Biological Chemistry 291:7951–7960. DOI: https://doi.org/10.1074/ jbc.M115.693408 Kanehisa M, Sato Y, Furumichi M, Morishima K, Tanabe M. 2019. New approach for understanding genome variations in KEGG. Nucleic Acids Research 47:D590–D595. DOI: https://doi.org/10.1093/nar/gky962, PMID: 30321428 Kleifeld O, Doucet A, auf dem Keller U, Prudova A, Schilling O, Kainthan RK, Starr AE, Foster LJ, Kizhakkedathu JN, Overall CM. 2010. Isotopic labeling of terminal amines in complex samples identifies protein N-termini and protease cleavage products. Nature Biotechnology 28:281–288. DOI: https://doi.org/10.1038/nbt.1611, PMID: 20208520 Komori T. 2010. Regulation of bone development and extracellular matrix protein genes by RUNX2. Cell and Tissue Research 339:189–195. DOI: https://doi.org/10.1007/s00441-009-0832-8, PMID: 19649655 Lam T-k. 2013. Fate of Notochord Descendent Cells in the Intervertebral Disc. Open Dissertation Press. Lau D, Elezagic D, Hermes G, Mo¨ rgelin M, Wohl AP, Koch M, Hartmann U, Ho¨ llriegl S, Wagener R, Paulsson M, Streichert T, Klatt AR. 2018. The cartilage-specific lectin C-type lectin domain family 3 member A (CLEC3A) enhances tissue plasminogen activator–mediated plasminogen activation. Journal of Biological Chemistry 293: 203–214. DOI: https://doi.org/10.1074/jbc.M117.818930 Lee CG, Da Silva CA, Dela Cruz CS, Ahangari F, Ma B, Kang MJ, He CH, Takyar S, Elias JA. 2011. Role of chitin and chitinase/chitinase-like proteins in inflammation, tissue remodeling, and injury. Annual Review of Physiology 73:479–501. DOI: https://doi.org/10.1146/annurev-physiol-012110-142250, PMID: 21054166 Leijten JC, Bos SD, Landman EB, Georgi N, Jahr H, Meulenbelt I, Post JN, van Blitterswijk CA, Karperien M. 2013. GREM1, FRZB and DKK1 mRNA levels correlate with osteoarthritis and are regulated by osteoarthritis- associated factors. Arthritis Research & Therapy 15:R126. DOI: https://doi.org/10.1186/ar4306, PMID: 242 86177 Li C, Hancock MA, Sehgal P, Zhou S, Reinhardt DP, Philip A. 2016. Soluble CD109 binds TGF-b and antagonizes TGF-b signalling and responses. Biochemical Journal 473:537–547. DOI: https://doi.org/10.1042/BJ20141488 Lo´ pez-Otı´nC, Overall CM. 2002. Protease degradomics: a new challenge for proteomics. Nature Reviews Molecular Cell Biology 3:509–519. DOI: https://doi.org/10.1038/nrm858, PMID: 12094217 Lu Y, Qiao L, Lei G, Mira RR, Gu J, Zheng Q. 2014. Col10a1 gene expression and chondrocyte hypertrophy during skeletal development and disease. Frontiers in Biology 9:195–204. DOI: https://doi.org/10.1007/s11515- 014-1310-6 Markmann A, Hausser H, Scho¨ nherr E, Kresse H. 2000. Influence of decorin expression on transforming growth factor-beta-mediated collagen gel retraction and biglycan induction. Matrix Biology 19:631–636. DOI: https:// doi.org/10.1016/S0945-053X(00)00097-4, PMID: 11102752 Maseda M, Yamaguchi H, Kuroda K, Mitsumata M, Tokuhashi Y, Esumi M. 2016. Proteomic analysis of human intervertebral disc degeneration. Journal of Nihon University Medical Association 75:16–21. DOI: https://doi. org/10.4264/numa.75.1_16

Tam, Chen, et al. eLife 2020;9:e64940. DOI: https://doi.org/10.7554/eLife.64940 34 of 37 Tools and resources Computational and Systems Biology

McCann MR, Patel P, Frimpong A, Xiao Y, Siqueira WL, Se´guin CA. 2015. Proteomic signature of the murine intervertebral disc. PLOS ONE 10:e0117807. DOI: https://doi.org/10.1371/journal.pone.0117807, PMID: 256 89066 Melas IN, Chairakaki AD, Chatzopoulou EI, Messinis DE, Katopodi T, Pliaka V, Samara S, Mitsos A, Dailiana Z, Kollia P, Alexopoulos LG. 2014. Modeling of signaling pathways in chondrocytes based on phosphoproteomic and cytokine release data. Osteoarthritis and Cartilage 22:509–518. DOI: https://doi.org/10.1016/j.joca.2014. 01.001, PMID: 24457104 Minogue BM, Richardson SM, Zeef LA, Freemont AJ, Hoyland JA. 2010. Characterization of the human nucleus pulposus cell phenotype and evaluation of novel marker gene expression to define adult stem cell differentiation. Arthritis & Rheumatism 62:3695–3705. DOI: https://doi.org/10.1002/art.27710, PMID: 2072201 8 Molinos M, Almeida CR, Caldeira J, Cunha C, Gonc¸alves RM, Barbosa MA. 2015. Inflammation in intervertebral disc degeneration and regeneration. Journal of the Royal Society Interface 12:20150429. DOI: https://doi.org/ 10.1098/rsif.2015.0429 Munir S, Rade M, Ma¨ a¨ tta¨ JH, Freidin MB, Williams FMK. 2018. Intervertebral disc biology: genetic basis of disc degeneration. Current Molecular Biology Reports 4:143–150. DOI: https://doi.org/10.1007/s40610-018-0101-2, PMID: 30464887 Naba A, Clauser KR, Hoersch S, Liu H, Carr SA, Hynes RO. 2012. The matrisome: in silico definition and in vivo characterization by proteomics of normal and tumor extracellular matrices. Molecular & Cellular Proteomics : MCP 11:M111.014647. DOI: https://doi.org/10.1074/mcp.M111.014647, PMID: 22159717 Nakai T, Sakai D, Nakamura Y, Nukaga T, Grad S, Li Z, Alini M, Chan D, Masuda K, Ando K, Mochida J, Watanabe M. 2016. CD146 defines commitment of cultured annulus fibrosus cells to express a contractile phenotype. Journal of Orthopaedic Research 34:1361–1372. DOI: https://doi.org/10.1002/jor.23326, PMID: 27273299 Nakamichi R, Kataoka K, Asahara H. 2018. Essential role of mohawk for tenogenic tissue homeostasis including spinal disc and periodontal ligament. Modern Rheumatology 28:933–940. DOI: https://doi.org/10.1080/ 14397595.2018.1466644, PMID: 29667905 Naveau S, Poynard T, Benattar C, Bedossa P, Chaput JC. 1994. Alpha-2-macroglobulin and hepatic fibrosis diagnostic interest. Digestive Diseases and Sciences 39:2426–2432. DOI: https://doi.org/10.1007/ BF02087661, PMID: 7525168 Nerlich AG, Schaaf R, Wa¨ lchli B, Boos N. 2007. Temporo-spatial distribution of blood vessels in human lumbar intervertebral discs. European Spine Journal 16:547–555. DOI: https://doi.org/10.1007/s00586-006-0213-x, PMID: 16947015 Newell N, Little JP, Christou A, Adams MA, Adam CJ, Masouros SD. 2017. Biomechanics of the human intervertebral disc: a review of testing techniques and results. Journal of the Mechanical Behavior of Biomedical Materials 69:420–434. DOI: https://doi.org/10.1016/j.jmbbm.2017.01.037, PMID: 28262607 Ong SE, Blagoev B, Kratchmarova I, Kristensen DB, Steen H, Pandey A, Mann M. 2002. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Molecular & Cellular Proteomics 1:376–386. DOI: https://doi.org/10.1074/mcp.M200025-MCP200, PMID: 12118079 O¨ nnerfjord P, Khabut A, Reinholt FP, Svensson O, Heinega˚ rd D. 2012. Quantitative proteomic analysis of eight cartilaginous tissues reveals characteristic differences as well as similarities between subgroups. Journal of Biological Chemistry 287:18913–18924. DOI: https://doi.org/10.1074/jbc.M111.298968 Park JS, Chu JS, Tsou AD, Diop R, Tang Z, Wang A, Li S. 2011. The effect of matrix stiffness on the differentiation of mesenchymal stem cells in response to TGF-b. Biomaterials 32:3921–3930. DOI: https://doi. org/10.1016/j.biomaterials.2011.02.019, PMID: 21397942 Peix L, Evans IC, Pearce DR, Simpson JK, Maher TM, McAnulty RJ. 2018. Diverse functions of clusterin promote and protect against the development of pulmonary fibrosis. Scientific Reports 8:1906. DOI: https://doi.org/10. 1038/s41598-018-20316-1, PMID: 29382921 Pfirrmann CW, Metzdorf A, Zanetti M, Hodler J, Boos N. 2001. Magnetic resonance classification of lumbar intervertebral disc degeneration. Spine 26:1873–1878. DOI: https://doi.org/10.1097/00007632-200109010- 00011, PMID: 11568697 R Development Core Team. 2013. R: A language and environment for statistical computing. 2.6.2. Vienna, Austria, R Foundation for Statistical Computing. https://www.R-project.org/ Rajasekaran S, Tangavel C, K.S. SVA, Soundararajan DCR, Nayagam SM, Matchado MS, Raveendran M, Shetty AP, Kanna RM, Dharmalingam K. 2020. Inflammaging determines health and disease in lumbar discs-evidence from differing proteomic signatures of healthy, aging, and degenerating discs. The Spine Journal 20:48–59. DOI: https://doi.org/10.1016/j.spinee.2019.04.023, PMID: 31125691 Rajesh D, Dahia CL. 2018. Role of sonic hedgehog signaling pathway in intervertebral disc formation and maintenance. Current Molecular Biology Reports 4:173–179. DOI: https://doi.org/10.1007/s40610-018-0107-9, PMID: 30687592 Ranjani V, Sreemol G, Muthurajan R, Natesan S, Gnanam R, Kanna RM, Rajasekaran S. 2016. Proteomic analysis of degenerated intervertebral disc- identification of biomarkers of degenerative disc disease and development of proteome database. Global Spine Journal 6:s-0036-1582631. DOI: https://doi.org/10.1055/s-0036-1582631 Rauniyar N, Yates JR. 2014. Isobaric labeling-based relative quantification in shotgun proteomics. Journal of Proteome Research 13:5293–5309. DOI: https://doi.org/10.1021/pr500880b, PMID: 25337643 Riester SM, Lin Y, Wang W, Cong L, Mohamed Ali A-M, Peck SH, Smith LJ, Currier BL, Clark M, Huddleston P, Krauss W, Yaszemski MJ, Morrey ME, Abdel MP, Bydon M, Qu W, Larson AN, van Wijnen AJ, Nassr A. 2018.

Tam, Chen, et al. eLife 2020;9:e64940. DOI: https://doi.org/10.7554/eLife.64940 35 of 37 Tools and resources Computational and Systems Biology

RNA sequencing identifies gene regulatory networks controlling extracellular matrix synthesis in intervertebral disk tissues. Journal of Orthopaedic Research 36:1356–1369. DOI: https://doi.org/10.1002/jor.23834 Risbud MV, Schoepflin ZR, Mwale F, Kandel RA, Grad S, Iatridis JC, Sakai D, Hoyland JA. 2015. Defining the phenotype of young healthy nucleus pulposus cells: recommendations of the spine research interest group at the 2014 annual ORS meeting. Journal of Orthopaedic Research 33:283–293. DOI: https://doi.org/10.1002/jor. 22789, PMID: 25411088 Robinson KA, Sun M, Barnum CE, Weiss SN, Huegel J, Shetye SS, Lin L, Saez D, Adams SM, Iozzo RV, Soslowsky LJ, Birk DE. 2017. Decorin and biglycan are necessary for maintaining collagen fibril structure, fiber realignment, and mechanical properties of mature tendons. Matrix Biology 64:81–93. DOI: https://doi.org/10. 1016/j.matbio.2017.08.004, PMID: 28882761 Rodrigues-Pinto R, Berry A, Piper-Hanley K, Hanley N, Richardson SM, Hoyland JA. 2016. Spatiotemporal analysis of putative notochordal cell markers reveals CD24 and keratins 8, 18, and 19 as notochord-specific markers during early human intervertebral disc development. Journal of Orthopaedic Research 34:1327–1340. DOI: https://doi.org/10.1002/jor.23205, PMID: 26910849 Rodriguez AG, Slichter CK, Acosta FL, Rodriguez-Soto AE, Burghardt AJ, Majumdar S, Lotz JC. 2011. Human disc nucleus properties and vertebral endplate permeability. Spine 36:512–520. DOI: https://doi.org/10.1097/ BRS.0b013e3181f72b94, PMID: 21240044 Rubin DI. 2007. Epidemiology and risk factors for spine pain. Neurologic Clinics 25:353–371. DOI: https://doi. org/10.1016/j.ncl.2007.01.004, PMID: 17445733 Rutges JP, Duit RA, Kummer JA, Oner FC, van Rijen MH, Verbout AJ, Castelein RM, Dhert WJ, Creemers LB. 2010. Hypertrophic differentiation and calcification during intervertebral disc degeneration. Osteoarthritis and Cartilage 18:1487–1495. DOI: https://doi.org/10.1016/j.joca.2010.08.006, PMID: 20723612 Saito T, Ikeda T, Nakamura K, Chung UI, Kawaguchi H. 2007. S100A1 and S100B, transcriptional targets of SOX trio, inhibit terminal differentiation of chondrocytes. EMBO Reports 8:504–509. DOI: https://doi.org/10.1038/sj. embor.7400934, PMID: 17396138 Sakai D, Nakamura Y, Nakai T, Mishima T, Kato S, Grad S, Alini M, Risbud MV, Chan D, Cheah KS, Yamamura K, Masuda K, Okano H, Ando K, Mochida J. 2012. Exhaustion of nucleus pulposus progenitor cells with ageing and degeneration of the intervertebral disc. Nature Communications 3:1264. DOI: https://doi.org/10.1038/ ncomms2226, PMID: 23232394 Saleem S, Aslam HM, Rehmani MA, Raees A, Alvi AA, Ashraf J. 2013. Lumbar disc degenerative disease: disc degeneration symptoms and magnetic resonance image findings. Asian Spine Journal 7:322–334. DOI: https:// doi.org/10.4184/asj.2013.7.4.322, PMID: 24353850 Sarath Babu N, Krishnan S, Brahmendra Swamy CV, Venkata Subbaiah GP, Gurava Reddy AV, Idris MM. 2016. Quantitative proteomic analysis of normal and degenerated human intervertebral disc. The Spine Journal 16: 989–1000. DOI: https://doi.org/10.1016/j.spinee.2016.03.051, PMID: 27125197 Schneiderman G, Flannigan B, Kingston S, Thomas J, Dillin WH, Watkins RG. 1987. Magnetic resonance imaging in the diagnosis of disc degeneration: correlation with discography. Spine 12:276–281. DOI: https://doi.org/10. 1097/00007632-198704000-00016, PMID: 2954224 Scott JL, Gabrielides C, Davidson RK, Swingler TE, Clark IM, Wallis GA, Boot-Handford RP, Kirkwood TB, Taylor RW, Talyor RW, Young DA. 2010. Superoxide dismutase downregulation in osteoarthritis progression and end- stage disease. Annals of the Rheumatic Diseases 69:1502–1510. DOI: https://doi.org/10.1136/ard.2009. 119966, PMID: 20511611 Silagi ES, Shapiro IM, Risbud MV. 2018. Glycosaminoglycan synthesis in the nucleus pulposus: dysregulation and the pathogenesis of disc degeneration. Matrix Biology 71-72:368–379. DOI: https://doi.org/10.1016/j.matbio. 2018.02.025, PMID: 29501510 Song YQ, Cheung KM, Ho DW, Poon SC, Chiba K, Kawaguchi Y, Hirose Y, Alini M, Grad S, Yee AF, Leong JC, Luk KD, Yip SP, Karppinen J, Cheah KS, Sham P, Ikegawa S, Chan D. 2008. Association of the asporin D14 allele with lumbar-disc degeneration in asians. The American Journal of Human Genetics 82:744–747. DOI: https://doi.org/10.1016/j.ajhg.2007.12.017, PMID: 18304494 Song Y-Q, Karasugi T, Cheung KMC, Chiba K, Ho DWH, Miyake A, Kao PYP, Sze KL, Yee A, Takahashi A, Kawaguchi Y, Mikami Y, Matsumoto M, Togawa D, Kanayama M, Shi D, Dai J, Jiang Q, Wu C, Tian W, et al. 2013. Lumbar disc degeneration is linked to a carbohydrate sulfotransferase 3 variant. Journal of Clinical Investigation 123:4909–4917. DOI: https://doi.org/10.1172/JCI69277 Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. 2005. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. PNAS 102:15545–15550. DOI: https://doi.org/10.1073/pnas.0506580102, PMID: 16199517 Subramanian A, Schilling TF. 2014. Thrombospondin-4 controls matrix assembly during development and repair of myotendinous junctions. eLife 3:e02372. DOI: https://doi.org/10.7554/eLife.02372 Sun Z, Liu B, Luo ZJ. 2020. The immune privilege of the intervertebral disc: implications for intervertebral disc degeneration treatment. International Journal of Medical Sciences 17:685–692. DOI: https://doi.org/10.7150/ ijms.42238, PMID: 32210719 Taha IN, Naba A. 2019. Exploring the extracellular matrix in health and disease using proteomics. Essays in Biochemistry 63:417–432. DOI: https://doi.org/10.1042/EBC20190001, PMID: 31462529 Takao T, Iwaki T. 2002. A comparative study of localization of heat shock protein 27 and heat shock protein 72 in the developmental and degenerative intervertebral discs. Spine 27:361–367. DOI: https://doi.org/10.1097/ 00007632-200202150-00007, PMID: 11840100

Tam, Chen, et al. eLife 2020;9:e64940. DOI: https://doi.org/10.7554/eLife.64940 36 of 37 Tools and resources Computational and Systems Biology

Tam V. 2021. DIPPER. Software Heritage. swh:1:rev:49e4da79786de1dfa496d7c7472343f3b865696c. https:// archive.softwareheritage.org/swh:1:dir:48aa545b7d49a45e1fb2e8f1ac92f07ed38d95e9;origin=https://github.com/ hkudclab/DIPPER;visit=swh:1:snp:ae2d5e4ab6a839da8bbf84579f451eb9f2c06277;anchor=swh:1:rev: 49e4da79786de1dfa496d7c7472343f3b865696c/ Taye N, Karoulias SZ, Hubmacher D. 2020. The "other" 15-40%: The Role of Non-Collagenous Extracellular Matrix Proteins and Minor Collagens in Tendon. Journal of Orthopaedic Research 38:23–35. DOI: https://doi.org/10. 1002/jor.24440, PMID: 31410892 Teraguchi M, Yoshimura N, Hashizume H, Muraki S, Yamada H, Minamide A, Oka H, Ishimoto Y, Nagata K, Kagotani R, Takiguchi N, Akune T, Kawaguchi H, Nakamura K, Yoshida M. 2014. Prevalence and distribution of intervertebral disc degeneration over the entire spine in a population-based cohort: the wakayama spine study. Osteoarthritis and Cartilage 22:104–110. DOI: https://doi.org/10.1016/j.joca.2013.10.019, PMID: 24239943 Tibshirani R. 1996. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B 58:267–288. DOI: https://doi.org/10.1111/j.2517-6161.1996.tb02080.x Trougakos IP. 2013. The molecular chaperone apolipoprotein J/clusterin as a sensor of oxidative stress: implications in therapeutic approaches - a mini-review. Gerontology 59:514–523. DOI: https://doi.org/10.1159/ 000351207, PMID: 23689375 Urban JP, Smith S, Fairbank JC. 2004. Nutrition of the intervertebral disc. Spine 29:2700–2709. DOI: https://doi. org/10.1097/01.brs.0000146499.97948.52, PMID: 15564919 van den Akker GGH, Koenders MI, van de Loo FAJ, van Lent P, Blaney Davidson E, van der Kraan PM. 2017. Transcriptional profiling distinguishes inner and outer annulus fibrosus from nucleus pulposus in the bovine intervertebral disc. European Spine Journal 26:2053–2062. DOI: https://doi.org/10.1007/s00586-017-5150-3, PMID: 28567592 van der Kraan PM, van den Berg WB. 2012. Chondrocyte hypertrophy and osteoarthritis: role in initiation and progression of cartilage degeneration? Osteoarthritis and Cartilage 20:223–232. DOI: https://doi.org/10.1016/ j.joca.2011.12.003, PMID: 22178514 Vaquerizas JM, Kummerfeld SK, Teichmann SA, Luscombe NM. 2009. A census of human transcription factors: function, expression and evolution. Nature Reviews Genetics 10:252–263. DOI: https://doi.org/10.1038/ nrg2538, PMID: 19274049 Veras MA, McCann MR, Tenn NA, Se´guin CA. 2020. Transcriptional profiling of the murine intervertebral disc and age-associated changes in the nucleus pulposus. Connective Tissue Research 61:63–81. DOI: https://doi. org/10.1080/03008207.2019.1665034, PMID: 31597481 Wang XL, Hou L, Zhao CG, Tang Y, Zhang B, Zhao JY, Wu YB. 2019a. Screening of genes involved in epithelial- mesenchymal transition and differential expression of complement-related genes induced by PAX2 in renal tubules. Nephrology 24:263–271. DOI: https://doi.org/10.1111/nep.13216, PMID: 29280536 Wang Y, Fan X, Xing L, Tian F. 2019b. Wnt signaling: a promising target for osteoarthritis therapy. Cell Communication and Signaling 17:97. DOI: https://doi.org/10.1186/s12964-019-0411-x, PMID: 31420042 Wis´niewski JR, Hein MY, Cox J, Mann M. 2014. A "proteomic ruler" for protein copy number and concentration estimation without spike-in standards. Molecular & Cellular Proteomics 13:3497–3506. DOI: https://doi.org/10. 1074/mcp.M113.037309, PMID: 25225357 Wuertz K, Vo N, Kletsas D, Boos N. 2012. Inflammatory and catabolic signalling in intervertebral discs: the roles of NF-kB and MAP Kinases. European Cells and Materials 23:102–120. DOI: https://doi.org/10.22203/eCM. v023a08 Wyatt AR, Yerbury JJ, Wilson MR. 2009. Structural characterization of Clusterin-Chaperone client protein complexes. Journal of Biological Chemistry 284:21920–21927. DOI: https://doi.org/10.1074/jbc.M109.033688 Yates B, Braschi B, Gray KA, Seal RL, Tweedie S, Bruford EA. 2017. Genenames.org: the HGNC and VGNC resources in 2017. Nucleic Acids Research 45:D619–D625. DOI: https://doi.org/10.1093/nar/gkw1033, PMID: 27799471 Yee A, Lam MP, Tam V, Chan WC, Chu IK, Cheah KS, Cheung KM, Chan D. 2016. Fibrotic-like changes in degenerate human intervertebral discs revealed by quantitative proteomic analysis. Osteoarthritis and Cartilage 24:503–513. DOI: https://doi.org/10.1016/j.joca.2015.09.020, PMID: 26463451 Zhu M, Tang D, Wu Q, Hao S, Chen M, Xie C, Rosier RN, O’Keefe RJ, Zuscik M, Chen D. 2009. Activation of beta-catenin signaling in articular chondrocytes leads to osteoarthritis-like phenotype in adult beta-catenin conditional activation mice. Journal of Bone and Mineral Research 24:12–21. DOI: https://doi.org/10.1359/ jbmr.080901, PMID: 18767925 Zhu S, Qiu H, Bennett S, Kuek V, Rosen V, Xu H, Xu J. 2019. Chondromodulin-1 in health, osteoarthritis, Cancer, and heart disease. Cellular and Molecular Life Sciences 76:4493–4502. DOI: https://doi.org/10.1007/s00018- 019-03225-y, PMID: 31317206

Tam, Chen, et al. eLife 2020;9:e64940. DOI: https://doi.org/10.7554/eLife.64940 37 of 37