FGFR4 protein, human

In RNA-Seq analysis, the q value is an adjusted P value, taking into account the FDR. A P value of 0.05 indicates that 5% of all tests will be false positives. An FDR-adjusted P value of 0.05 implies that 5% of the tests found to be statistically significant (for example, by P value) will be false positives. Therefore, FDR has a greater power than P value, and we have mainly relied on FDR to gauge DEGs. To define DEGs, we used very stringent statistic threshold of ≥2 FC and FDR <0.05 to generate manageable lists in order for us to perform manual curation to classify each DEG in each cell type into non-redundant functional categories. Using the above statistical threshold, we identified a consensus of 853 DEGs upregulated in basal and 940 DEGs in luminal cells (Supplementary Data 1). Notably, to avoid the misunderstanding that genes not presented in the ‘stringent' lists are not DEGs, we also listed genes that passed a relatively loose but still statistically significant cutoff (that is, FC≥2 and P<0.05) in Supplementary Data 1. This latter cutoff resulted in more DEGs in basal (n=1,432) and luminal (n=1,548) cell populations (Supplementary Data 1). For example, FGFR3 (Fig. 3a) and some Pol I complex subunits (Fig. 4e; for example, POLR1B (P=0.006, FDR=0.069), POLR1C (P=0.006, FDR=0.069), NIP7 (P=0.005, FDR=0.060), and ESF1 (P=0.006, FDR=0.063) were not in the list with FDR<0.05, but were in the list with P<0.05. For Fig. 3a, the reason we chose FGFR3 (P=0.006, FDR=0.07) for demonstration was its abundance over other differentially expressed FGFRs (for example, the mean FPKM in basal cell, FGFR3=11 versus FGFR4=1), although its FDR was slightly above the stringent cutoff of 0.05. To get more reliable and manageable results, we mainly used the fewer DEGs lists for bioinformatics analysis.
For Fig. 1i, we identified the top 50 putative marker genes specific for each lineage inferred from transcriptomes based on both relative differential expression (FC) and absolute expression levels (normalized read counts). To increase the confidence of this selection, we scanned the genes from the stringent DEGs lists. Thus, the genes showing high-RNA expression (normalized read counts>300) in both cell types, regardless of the differential FC, would be excluded due to the high probability of protein expression in both cell types. Likewise, genes showing high FC difference between the two cell types but having minimal RNA expression in either cell type (that is, normalized read counts<300, indicating the less probability of robust protein expression) would also be eliminated. Note that normalized read counts of 300 (quite high) is an arbitrary set-up to increase the reliability of this selection. Using these criteria, we could identify >100 genes unique for each cell type, and the top 50 were shown in Fig. 1i. Notably, FGFR3 is not in the top 50, but we included it in Fig. 1i owing to the experimental data and for the reasons discussed above.

Habituation to the tests was carried out 2 weeks before surgery. Behavioral assessments were performed 1 week and 24 h prior to surgery to set pre-operative values. Then, behavioral analyses were carried out at 3 days, 5 days, 1 week, and once a week over 6 weeks after surgery.

Open field: Animals were placed in a 50 × 50 cm square arena. Spontaneous motor activity was video-recorded over 10 min. The first two minutes were considered habituation time and were systematically excluded from analysis. Recordings were first performed on a smooth ground and then repeated on sandpaper surfaces with two types of granularities, i.e., 50 and 240 mean grain size/µm (adapted from [34 (link),35 (link)]). The following parameters were analyzed: time spent by zone (s), speed by zone (cm/s), and time of immobility (s) (defined as no movement for more than 2 s). Zones were set as follow: total arena, arena center, arena periphery for smooth surface and zone of granularity 50, zone of granularity 240 for sandpaper surface. Center region size was set as 20 × 20 cm, and the periphery corresponds to the remaining area. Zones of granularities 50 and 240 were designed as 25 × 25 cm square and placed in alternance. Ethotrack software (Innovation Net, Tiranges, France) was used for the automatized video tracking and analysis.

CatWalk^TM: A dynamic walking pattern was analyzed using the CatWalk™ test (CatWalk XT™, Noldus, Wageningen, The Netherlands). Animals walked through a corridor on a backlighted glass plate. Paw placements were recorded by a camera placed under the glass plate. Six runs per sessions were recorded. Runs were analyzed only if the following criteria were met: average speed comprised between 5 and 30 cm/s, and combined to a maximum speed variation of 70% (adapted from [36 (link)]). Several parameters were analyzed, including base of support, print position, and max contact. Animals included in the experimental and control groups had similar weights and average motion speed, allowing for equivalent detection and comparison between groups, as previously described [36 (link)]. Additionally, we have quantified the percentage of detected ipsilateral hind paws prior traumatism and over the first week after SCI.

Number of mice included in the behavioral study: 18 C57BL6/6J underwent SCI and pHIV-Fgfr4 vector injections (12 females and 6 males, referred to as experimental mice); 19 mice underwent SCI and pHIV-mCherry vector injections (12 females and 7 males, referred to as control mice).