Figure 3. The axonal-like protrusions contribute to the migratory ability of SCLC cells in culture.

(A) Quantification of the number of cells with protrusions when mGFP-labeled N2N1G mSCLC cells were allowed to grow into a cell-free scratch generated in monolayer cultures under Matrigel. N = 3 independent experiments (shControl, N = 3 per experiment, total N = 9 plotted together). An unpaired t-test was used for statistical analysis and p-values are shown. Only significant p-values are shown. The dotted line represents a 60% reduction compared to the mean value of the controls. (B) Representative images of the data quantified in (A) and (C) with knock-down of Gap43. Scale bars, 100 μm. (C) Quantification of the migration of cells with protrusions when mGFP-labeled N2N1G mSCLC cells were allowed to grow into a cell-free scratch generated in monolayer cultures under Matrigel. N = 3 independent experiments. An unpaired t-test was used for statistical analysis and p-values are shown. Only significant p-values are shown. The dotted line represents a 60% reduction compared to the mean value of the controls. (D) Correlation of the data in (A) and (C) using the mean value for each knock-down. Pearson correlation R² value is shown. (E and H) Immunoblot analysis of GAP43 or FEZ1 levels, respectively, in control and knock-down 16T mSCLC cells. HSP90 is a loading control. (F and I) Quantification of the number of cells with protrusions as in (A) with 16T mSCLC cells and Gap43 or Fez1 knock-down, respectively (N = 3). An unpaired t-test was used for statistical analysis and p-values are shown. (G and J) Quantification of the migration of cells with protrusions as in (B) with 16T mSCLC cells and Gap43 or Fez1 knock-down, respectively (N = 3). An unpaired t-test was used for statistical analysis and p-values are shown.

Figure 3—figure supplement 1. The expression of the 13 genes selected for their possible role in the formation of protrusions is in part regulated by NFIB.

(A) mRNA levels of candidate genes in human primary SCLC tumors (RNA-seq from George et al., 2015). (B) Network representation of the 13 candidates. Edges in the STRING analysis represent protein-protein associations but do not necessarily mean that they physically bind to each other. Blue edges represent known interactions from curated databases. Pink edges represent known experimentally-validated interactions. Others are predicted interactions, including text mining and co-expression (see string-db.org). (C) Spearman correlation of the 13 genes with key genetic drivers of SCLC phenotypes. The numbers for these analyses are from George et al. (2015), and Yang et al. (2018) (see also Supplementary file 2–tables 2-3). Note the higher correlation overall with NFIB expression. MAP1B was not identified in the mouse RNA data. (D) Gene expression analysis of three key genetic drivers of SCLC phenotypes in five human SCLC cell lines, along with the protrusions phenotype (see Figure 1—figure supplement 1 and data not shown). YAP1 and POU2F3 are not detected in these cells lines (data from CCLE RNA-seq analyses, https://www.ebi.ac.uk/gxa/experiments/E-MTAB-2770/Results). Note that the growth of protrusions does not correlate with ASCL1 or NEUROD1 expression. (E) Gene expression analysis (log two fold-change) of the 13 genes following NFIB knock-down in NFIB-high 16 T cells (which grow protrusions) and NFIB over-expression in NFIB-low KP22 cells (which don’t grow protrusions). RNA-seq data and immunoblot validation of the immunoblots for NFIB are from Denny et al. (2016). (F) Representative bright field image and quantification of the number of protrusions when 16T mSCLC cells were allowed to grow into a cell-free scratch generated in monolayer cultures under Matrigel with control shRNA or NFIB knock-own (N = 3). Scale bars, 100 μm. *p<0.05, **p<0.01, p<0.005, student t-test. (G) Representative bright field image of KP22 mSCLC cells with ectopic expression of GFP (control) of NFIB. Note the absence of protrusions (as quantified in Figure 1B) (N = 1 with three technical replicates). For (F) and (G), the immunoblot analysis of NFIB knock-down and ectopic expression in 16T and KP22 cells can be found in Denny et al. (2016).

Figure 3—figure supplement 2. The 13 genes selected for their possible role in the formation of protrusions are expressed in human SCLC but do not play a key role in the expansion of SCLC cell populations.

(A) DepMap analysis (depmap.org) of the requirement for the 13 candidate genes in 25 human SCLC cell lines (RNAi combined analysis). Note that in a number of cell lines, the knock-down of candidate genes results in a positive score, indicative of a better expansion upon knock-down. Even in cases where the scores are negative, the negative values are small (the data for the genes coding for the CHK1 and WEE1 kinases, which are considered therapeutic targets in SCLC, are shown on the right hand side). (B) Representative images of immunohistochemistry (IHC) for GAP43 (brown) on human SCLC tissue microarrays (N = 79 human samples analyzed). The signal was evaluated by a certified pathologist (K.C.) and the scores are indicated. Hematoxylin (blue) stains the nuclei of the cells. Scale bars, 50 μm. (C) Heat map gene expression analysis of 12/13 genes (Reln was not detected) in N2N1G and KP22 cells. Note that the growth of protrusions in N2N1G cells correlates with an overall increase in the expression of the gene signature. Data are log2 values from RNA-seq.

Figure 3—figure supplement 3. Knock-down of GAP43 and FEZ1 disrupts the formation of protrusions and cell migration in mouse SCLC cell lines in culture.

(A) Representative images of the data quantified in Figure 3A and C with knock-down of Fez1. Scale bars, 100 μm. (B–C) Immunoblot analysis of GAP43 (B) or FEZ1 (C) levels in control and knock-down N2N1G mSCLC cells. HSP90 is a loading control. (D–E) Representative images of the data with knock-down of Gap43 (D) or Fez1 (E) in 16 T cells. These data are quantified in Figure 3F–G (for GAP43) and Figure 3I–J (for FEZ1). The shControl targets GFP. Scale bars, 100 μm. (F) Representative images of 16T and N2N1G mouse SCLC cells in spheroids growing in 3D Matrigel with control shRNA or knock-down of Gap43 or Fez1. Scale bars, 50 μm. (G) Quantification of (F), determining the migration of cells out of the spheroids by measuring the area covered by cells outside of the spheroids relative to the size of each spheroid analyzed, 48 hr after plating the spheroids into the Matrigel. An unpaired t-test was used for statistical analysis. All p-values<0.0001 except one, as indicated.