## 4 Discussion  
Cells were isolated from the degenerated IVDs from a widely used rat model of lumbar disc puncture [24; 25; 26; 27; 28; 44]. Single-cell RNA-seq was then performed on over 149,282 non-degenerate and degenerate IVD cells harvested from two timepoints following surgery to allow the progression of IVD degeneration. In brief, we identified four major cell types in the non-degenerate and degenerative IVD tissues ofc 2 weeks following surgery, based on the identification of genes previously identified as \"classical\" cell-specific markers [15]. For example, IVD cells are closely clustered around classical \"chondrocyte-like\" markers including _Acan_, _Col2a1_, _Sox9_, _Comp_, and _Fmod_. The cell classifications here in the master clustering and sub-clustering schemes for IVD cells from CON populations are consistent with a subset of those identified previously for NP cells; differences in cell identities were not noted between CON populations at 2 and 8 weeks following surgery, only differences in cell numbers mapping to each cluster. Initial results that showed over 80% of all cells were identified as IVD cells at both 2 and 8 weeks, similar to a prior study showing that up to 99% of all sequenced cells mapped to a common transcriptomic profile [5]. The IVD cluster was then sub-clustered into NP, IAF, and OAF cells. For the markers chosen here for cell identification, the analysis revealed the absence of any singular genes that are specific to each subcluster. Rather, IVD cell type identification requires a set of genes that are either more or less highly expressed when compared to their spatial counterparts. Classical markers of IVD cells such as _Acan_, _Col1a1_, and _Col2a1_ and some NP-specific markers such as _Cd24_ were useful to support the sub-clustering scheme; however, expression levels exhibited a continuous distribution from inner to outer IVD. Prior studies have sometimes attempted to anatomically isolate NP tissues from adjacent inner AF prior to performing sc-RNA-seq analyses, yet yield the same outcome [5; 20; 22; 45], indicating the spatial overlapping nature of these tissues and the transition between cell populations in the IVD exists in a continuum. Instead, we found additional gene markers including _lgfp5_, _lgfp6_, _Lum_, _Myoc_, and _Fibin_ useful as supplements of the \"classical\" markers used to separate outer AF from NP populations within the IVD [5]. In the current study, effort was taken to evaluate sample-specific variability in the clustering results for the CON populations at each time point (e.g., Figure 2c). Studies that pool multiple tissue levels across animals to procure sufficient cells for a single sample for RNA-sequencing may be susceptible to one sample disproportionately driving the clustering outcome. While we had differences in cell yields between rats, the clustering scheme was valued for its ability to produce repeatable results across animals (e.g., 82-92% of all cells mapping to IVD cluster. Approaches that define correlation analyses for clustering schemes as used by [PERSON] and co-workers [16] would be a useful addition to all sc-RNA-seq analyses that employ multiple tissues from multiple subjects. With LDP, differences were observed in the numbers of cells associated with each identified cluster. We observed that the LDP samples contained 1.5 times more myeloid cells than CON tissue and that macrophages were the most prominent immune subtype present in the myeloid cluster (Figure 3c; Figure 5b). Macrophages have been shown to infiltrate the herniated or injured disc in multiple species [23; 42; 46; 47; 48; 49; 50]. Accordingly, _Cd4_ is a pan-macrophage marker in rats [51], _Cd68_ is involved in antigen presentation [52; 53], _Cd14_ is essential for macrophage phagocytosis [54], and _Lyz2_ is important for bacteriolysis [55]. In the LDP group, there were increased proportions of _Cd4+_ (_p_ < 0.00001), _Cd14+_ (_p_ < 0.05), _Cd68+_ (_p_ < 0.00001) and _Lyz2+_ (_p_ = 0.05) cells.