Abstract
Neuropathy is a feature more frequently observed in pancreatic ductal
adenocarcinoma (PDAC) than other tumors. Schwann cells, the most
prevalent cell type in peripheral nerves, migrate toward tumor cells
and associate with poor prognosis in PDAC. To unveil the effects of
Schwann cells on the neuro-stroma niche, here we perform single-cell
RNA-sequencing and microarray-based spatial transcriptome analysis of
PDAC tissues. Results suggest that Schwann cells may drive tumor cells
and cancer-associated fibroblasts (CAFs) to more malignant subtypes:
basal-like and inflammatory CAFs (iCAFs), respectively. Moreover, in
vitro and in vivo assays demonstrate that Schwann cells enhance the
proliferation and migration of PDAC cells via Midkine signaling and
promote the switch of CAFs to iCAFs via interleukin-1α. Culture of
tumor cells and CAFs with Schwann cells conditioned medium accelerates
PDAC progression. Thus, we reveal that Schwann cells induce malignant
subtypes of tumor cells and CAFs in the PDAC milieu.
Subject terms: Cancer microenvironment, Pancreatic cancer
__________________________________________________________________
The effects of Schwann cells on the neuro-stroma niche in pancreatic
ductal adenocarcinoma (PDAC) remain to be explored. Here, single-cell
RNA-sequencing and spatial transcriptome analysis of PDAC tissues
reveal that Schwann cells induce malignant subtypes of tumour cells and
cancer associated fibroblasts.
Introduction
Although advances in cancer research over the past few decades have
contributed to a steady increase in overall survival (OS) for many
types of cancers, the prognosis of pancreatic ductal adenocarcinoma
(PDAC) is still the worst in all cancers (5-years OS: 8–10%)^[68]1. The
lack of a reliable test for early diagnosis and/or resistance to
adjuvant therapy, are responsible for the dismal prognosis of
PDAC^[69]2. PDAC is characterized by a dense fibrotic stroma which was
composed of cancer-associated fibroblasts (CAFs), immunosuppressive
cells, endothelial cells, and nerve cells. CAFs secrete tropic factors
and extracellular matrix components, which lead to a low response rate
to first-line chemotherapy^[70]3. Comparing with CAFs, limited studies
have investigated nerve cells in the stroma of PDAC^[71]4–[72]6.
Neuropathy is a representative character of PDAC, which contributes to
pain, local recurrence, and worse prognosis in PDAC patients^[73]7.
Ablation of nerves using neurotoxin capsaicin optimizes the prognosis
of mice with PDAC^[74]8. Prominent perineural alterations were observed
during PDAC progression, such as an increase in the size of
intrapancreatic nerves (neural hypertrophy), neural density, and neural
remodeling^[75]9. Growing evidence suggests a positive interaction
between tumor cells and nerves. ADRB2-signaling pathway promotes the
secretion of nerve growth factor (NGF) and brain-derived neurotrophic
factor (BDNF) in PDAC cells, thereby increasing nerve density^[76]10.
In contrast, as one of the major sources of acetylcholine (Ach), nerves
activate cholinergic signaling in the gastric epithelium, inducing NGF
expression and promoting carcinogenesis^[77]11. Elevated Ach from the
vague nerve in PDAC reprograms the immune tumor microenvironment (TME)
by epigenetic repression of CCL5 in neoplastic cells, and impairs the
ability of PDAC cells to recruit CD8^+ T cells^[78]12. Neurons not only
secrete stimulatory factors to accelerate PDAC tumorigenesis but also
provide metabolic support to PDAC cells by releasing serine^[79]13.
Previous studies have predominantly focused on neurons, specifically
the interactions between neurons, PDAC cells, and immune
cells^[80]8,[81]13–[82]16. In addition to neurons, another key
component of neuropathy is the Schwann cell, the predominant cell type
in peripheral nerves. Remarkably, Schwann cells are detectable around
pancreatic intraepithelial neoplasia (PanIN) lesions both in humans and
mice^[83]17. Nonetheless, the Schwann cell’s contribution to the
pancreas has not been explored. Schwann cells are crucial for neural
repair and regeneration following nerve injury^[84]18. Similarly, after
invasive malignant cells instigated nerve trauma, Schwann cells undergo
a dynamic process of activation, which facilitates Schwann cells
proliferation and migration to cancer cells^[85]19. In addition,
Schwann cells migrate to malignant cells first before the latter invade
the nerves^[86]17. Schwann cells exhibit a robust tropism for cancer
cells and trigger nerve-cancer cell interactions^[87]20. Schwann cells
actively participate in serious cancer processes, including cancer
migration, invasion, immune exclusion, and transmission of cancer
pain^[88]21–[89]24. Through the NGF-Neurotrophic Receptor Tyrosine
Kinase 1 (TrkA)-Nerve Growth Factor Receptor (NGFR) axis, Schwann cells
exhibit robust chemotaxis towards neoplastic cells, in which
participate in neural regeneration around cancer cells^[90]17.
Inhibition of Schwann cell after cutaneous sensory nerve transection
significantly decreased melanoma tumor development^[91]25. Emerging
evidence suggests that Schwann cells promote cancer cell metastasis by
secreting C-X-C Motif Chemokine Ligand 5 (CXCL5), Interleukin 6 (IL-6),
and Transforming Growth Factor Beta 1 (TGF-β), or by direct cancer cell
contact^[92]26–[93]29. Active by c-Jun, Schwann cells could promote
PDAC cancer cell migration and invasion^[94]30.
Moreover, Schwann cells could also play an important role in
immunosuppressive TME by interacting with macrophages, mast cells,
dendritic cells (DCs), myeloid-derived suppressor cells (MDSCs), and
other immune cells^[95]24,[96]31. Although the role of Schwann cells in
cancer progression has been explored, the potential interactions
between Schwann cells and other main components of the TME, especially
CAFs in PDAC, is limited. The mechanism by which Schwann cells regulate
PDAC TME is yet to be elucidated.
In this work, immunohistochemistry (IHC) staining of PDAC tissue
microarray (TMA) shows the location of Schwann cells in tumors.
Further, integrated single-cell RNA sequencing (scRNA-seq) and
microarray-based spatial transcriptomics (ST) reveal the heterogeneity
of tumor cells and CAFs adjacent to Schwann cells. Furthermore, in
vitro and in vivo functional assays and pharmacological inhibition
validate the malignant function and the underlying mechanism of Schwann
cells.
Results
Prevalence of Schwann cells accumulation in the TME and its clinical
significance
To unveil the distribution of Schwann cells in PDAC, the pan-neural
marker Ubiquitin C-Terminal Hydrolase L1 (PGP9.5) and three traditional
Schwann cell markers, nerve growth factor receptor (p75NRT), S100
Calcium Binding Protein B (S100β) and glial fibrillary acidic protein
(GFAP)^[97]17,[98]29, were used for IHC staining and immunofluorescence
(IF) in a cohort of PDAC patients (n = 187). Markers of Schwann cells
(p75NRT, S100β, and GFAP) were co-localized with PGP9.5, suggesting
that the intrapancreatic nerves were accompanied by Schwann cells
(Fig. [99]1a and Supplementary Fig. [100]S1a, b). Histological sections
showed that Schwann cells were enriched in PDAC compared to non-tumor
tissues (Fig. [101]1b, c). As expected, the intrapancreatic area of
Schwann cells was increased; this was associated with worse prognosis
for PDAC (P = 0.021, Fig. [102]1d); and could be used as an independent
prognostic factor for OS (HR = 1.440, 95% CI: 1.029–2.015; P = 0.033)
(Fig. [103]1e). Using hematoxylin and eosin (H&E) staining, we observed
that Schwann cells were surrounded by stromal and tumor cells
(Fig.[104]1f and Supplementary Fig. [105]S1c). Bioinformatic analysis
of bulk RNA-seq data from The Cancer Genome Atlas (TCGA)-PAAD cohort
revealed that significant activation of epithelial-mesenchymal
transition (EMT) and carcinoma-associated fibroblasts were enriched in
tumor tissues with high expression of each Schwann cell
signature^[106]32–[107]34 (Fig.[108]1g and Supplementary
Fig. [109]S1d), suggesting that Schwann cells might communicate with
tumor cells and fibroblasts and promote malignant progression in PDAC.
Fig. 1. Prevalence of Schwann cell accumulation in the TME and its clinical
significance.
[110]Fig. 1
[111]Open in a new tab
a Representatives of IHC staining images with anti-PGP9.5, p75NRT, and
S100β. Scale bar, left, 100 µm; right, 50 µm. Images are representative
of 187 PDAC samples with similar results. b, c Violin chart of Schwann
cells area (b) or the number of regions used for calculating Schwann
cells area (c). For each violin, the minimum, first quartile, median,
third quartile, and maximum were displayed. n = 187. d OS analysis of
PDAC patients with low Schwann cells area (<3190 μm^2; n = 126) or high
area (≥3190 μm^2; n = 61) in PDAC patients (n = 187). The cutoff of
Schwann cells area was determined by X-Tile^[112]76. HR and 95% CI were
determined using the regression coefficient of the Cox model. e
Univariate and multivariate analysis of the OS of PDAC patients
(n = 187). The dot of the Forest plot represents the hazard ratio of
the Cox proportional hazards model, the error bars are two-sided 95%
confidence intervals. *P < 0.05. Independent variables with P < 0.05 in
the univariate analysis were included in the multivariate analysis. f
H&E staining showing nerves, stroma, and cancer cells in PDAC tumor and
normal tissue. asterisk, nerves; arrow, stroma; triangle, cancer cells.
Images are representative of three PDAC samples with similar results.
Scale bar, left, 100 µm; right, 50 µm. g GSEA plot showing that EMT and
carcinoma-associated fibroblasts were enriched in PDAC samples with
higher expression of Schwann cell signatures. Pathway enrichment
analysis was performed using data from the TCGA-PAAD cohort. NES
normalized enrichment score, corrected for multiple comparisons using
FDR method, P value were showed in plots. Statistical analysis:
unpaired two-sided t-test (b, c); log-rank test (d). Source data are
provided as a Source Data file.
Single-cell analysis reveals the heterogeneity of tumor cells and CAFs in
PDAC with Schwann cells accumulation
In the nervous system, Schwann cells play a substantial role in
maintaining homeostasis and possess the capacity for inflammation and
regeneration^[113]35. To explore whether Schwann cells shape the
heterogeneity of tumor cells and CAFs in the neuro-stroma milieu,
scRNA-seq was applied to four PDAC tissues with Schwann cells
accumulation, which was confirmed by tumor histopathology and IHC
(Fig. [114]2a, b and Supplementary Fig. S[115]2a, b). A total of 25,150
high-quality cell profiles were obtained for downstream analysis. Nine
major cell types were characterized: tumor cells, fibroblasts,
neutrophils, monocytes, T cells, B cells, mast cells, plasma cells, and
endothelial cells (Fig.[116]2c; Supplementary Fig. [117]S2c; and
Supplementary Data [118]1). Tumor cells and fibroblasts were selected
for further experiments.
Fig. 2. Single-cell analysis reveals the heterogeneity of tumor cells and
CAFs in PDAC with Schwann cell accumulation.
[119]Fig. 2
[120]Open in a new tab
a Representative H&E images of PDAC tissues with neural hypertrophy.
asterisk, nerves; arrow, stroma; triangle, cancer cells. Scale bar,
left, 100 µm; right, 50 µm. b Representatives of IHC staining images of
patient A with anti-PGP9.5, GFAP, and S100β. Scale bar, 50 µm. a, b
Images are representative of four PDAC samples with similar results. c
t-SNE plot depicting the major cell types identified by single-cell RNA
sequencing of four PDAC tissues. d UMAP plot showing two main
subclusters, basal-like and classical tumor cells from PDAC tissues. e
GO analysis showing the upregulated pathways in basal-like and
classical tumor cells. f The differentiation states of cancer cells
defined by CytoTRACE. For each boxplot, the first quartile, median, and
third quartile ± s.d were displayed. n = 4 PDAC patients. g OS analysis
of patients with low (n = 44) or with high (n = 44) basal-like
signature in the TCGA-PAAD cohort (n = 88). h UMAP plot showing three
main subclusters of fibroblasts, including iCAFs, myCAFs, and
stellate-like cells. i GO analysis showing the upregulated pathways in
iCAFs, myCAFs, and stellate-like cells. j Trajectory of fibroblasts
along pseudotime in a two-dimensional space. k Expression of NGFR in
the two cell fates of fibroblasts along pseudotime. The expression of
NGFR was determined by branch expression analysis. l Heatmap showing
the dynamic changes of gene expression along pseudotime. The
differentially expressed genes were clustered hierarchically into three
groups and the representative enriched pathways of each group were
shown. Statistical analysis: one-sided Fisher’s exact test (e, i);
two-sided Wilcoxon rank-sum test (f); log-rank test (g).
Tumor cells formed two distinct subclusters with unique gene signatures
upon uniform manifold approximation and projection (UMAP) analysis and
were annotated as basal-like and classical-like signatures previously
described in PDAC^[121]36–[122]38 (Fig. [123]2d, Supplementary
Fig. [124]S2c, d, and Supplementary Data [125]1). The classical subtype
showed higher expression of the epithelial pathway, and the basal-like
subtype was enriched in the basal-related pathway, as previously
described^[126]37,[127]38 (Fig. [128]2e and Supplementary
Fig. [129]S2e). Interestingly, the basal-like subtype was also enriched
in nerves corresponding signaling pathways such as axon guidance,
regulation of axonogenesis, and nerve development (Fig. [130]2e).
Cellular (Cyto) trajectory reconstruction analysis using gene counts
and expression (CytoTRACE)^[131]39 revealed that tumor cells with a
high basal-like signature score displayed poor differentiation compared
to those with a classical signature (Fig. [132]2f; Supplementary
Fig. [133]S2f, g; and Supplementary Data [134]1). Consistent with
previous studies, the basal-like signature was an unfavorable
prognostic factor in TCGA-PAAD cohort^[135]38,[136]40,[137]41
(Fig. [138]2g).
Although the existence of CAF heterogeneity has been established, the
phenotypic characteristics of CAFs in the neuro-stroma environment
remain unexplored. Unsupervised clustering analysis showed that
fibroblasts were categorized as stellate-like cells and CAFs. CAFs were
further identified as a myofibroblastic subset (myCAF) and inflammatory
subset (iCAF), according to previous studies (Fig. [139]2h;
Supplementary Fig. [140]S2c, h, i; and Supplementary
Data [141]1)^[142]6,[143]32,[144]33,[145]42. The pathways of ECM
organization and cell adhesion were enriched in the myCAF cluster,
whereas the pathways related to muscle contraction and actin
cytoskeleton were enriched in stellate-like cells (Fig. [146]2i and
Supplementary Fig. [147]S2j). Pancreatic stellate cells (PSCs)
functional genes^[148]32,[149]33 were enriched in stellate-like cells,
including FABP4, associated with lipid transport and retinoid storage,
and ADIRF, associated with adipogenesis (Supplementary Fig. [150]S2i).
Notably, the axon guidance pathway and NGFR, which could bind to NGF
and other neurotrophins, were also activated in the iCAF cluster
(Fig. [151]2i and Supplementary Fig. [152]S2j, k). To further
investigate the potential transitional process of CAFs in TME, monocle2
was applied to construct the pseudotime map of the fibroblast
developmental state trajectory. Consistent with most studies reported
that PSCs were the major source of CAFs in PDAC^[153]43, stellate-like
cells were the start of trajectories. iCAFs featured terminal
differentiation and were considered the end of one trajectory
(Fig. [154]2j and Supplementary Data [155]1). Moreover,
nerve-corresponding genes, including NGFR, were expressed dynamically
with pseudotime in CAFs (Fig. [156]2k and Supplementary Data [157]1).
Branched expression analysis modeling (BEAM) was performed to
investigate the two branches of CAFs^[158]44. The dynamic relationship
between stellate cells, iCAFs, and myCAFs further confirmed that matrix
organization-related genes were activated in myCAFs destined for cell
fate 1, while cell migration, nerves, and Schwann cell corresponding
pathways were enriched in iCAFs and the other myCAFs for cell fate 2
(Fig. [159]2l). Together, the PDAC milieu with neural hypertrophy and
Schwann cells accumulation consisted of malignant clusters of CAFs and
tumor cells, which may participate in nerve development and Schwann
cell recruitment during disease progression.
Similar to previous studies, we only detected a small Schwann-like
cells cluster (four cells, 0.0159%), with CDH19, PLP1, and SOX10 high
expression^[160]32,[161]33 (Supplementary Fig. [162]S2l). However, due
to the scarcity of cell numbers, no further investigation was performed
on these putative Schwann cells. Alternatively, a spatial transcriptome
was performed to explore the location and heterogeneity of cell types
in the neuro-stroma niche.
Microarray-based spatial transcriptomics reveals that Schwann cells are
surrounded by iCAFs and basal-like tumor cells
A series of cryosections of four fresh PDAC tissues were used for
spatial transcriptome (ST) analysis. H&E staining was performed to
validate the accurate location of nerves, stroma, and epithelium. The
regions of the nerves, stroma, and epithelium were annotated in each
slide according to the histological features (Fig. [163]3a and
Supplementary Fig. [164]S3a). Single-sample gene set enrichment
analysis (ssGSEA) was performed to quantify the spatial distribution of
fibroblasts and tumor cells using the corresponding markers in
scRNA-seq (Fig. [165]3b and Supplementary Fig. [166]S3b). By integrated
analysis of the two annotations, the regions of the stroma and
epithelium, were determined for the following analysis (Fig. [167]3b
and Supplementary Fig. [168]S3b). Unsupervised clustering analysis
identified cluster 19 with high expression of Schwann cell-related
pathways and markers (SOX10 and GAP-43) (Fig. [169]3c; Supplementary
Fig. [170]S3c–f; and Supplementary Data [171]2). As expected, most
nerve regions overlapped with the regions of Cluster 19 and high ssGSEA
score of Schwann cells signature was also enriched in nerve regions
(Fig. [172]3c).
Fig. 3. Microarray-based spatial transcriptomics reveals that Schwann cells
are surrounded by iCAFs and basal-like tumor cells.
[173]Fig. 3
[174]Open in a new tab
a Histological annotations of tumor cryosection from patient A. Images
are representative of four PDAC samples with similar results. Scale
bar, left, 1000 µm; right, 100 µm. b Stroma and epithelium regions were
defined by integrating the results of ssGSEA and histologic annotation.
c Schwann cell markers SOX10 was co-localized with nerve region in
Patients A (upper panel). The score of Schwann cell signature
determined by ssGSEA is high in nerve regions (lower panel). Scale bar,
1000 µm. d The scores of iCAF, myCAF, basal-like and classical
signatures determined by ssGSEA in the neuro-stroma niche. e Perineural
tier 1‒4 and other regions were defined according to the distance to a
nerve in the neuro-stroma niche. Scale bar, 1000 µm. f The ssGSEA
scores of iCAF, myCAF, basal-like and classical signature in the
perineural region (tier 1) compared with the other regions (tier 2‒4
and other regions). For each boxplot, the first quartile, median, and
third quartile ± s.d were displayed. Patients number = 4, nerves
number = 40. g The QuSAGE scores of iCAF, myCAF, basal-like, and
classical signature in tier 1‒4 and other regions in the neuro-stroma
niche. Statistical analysis: two-sided Wilcoxon rank-sum test (f).
Further, ssGSEA of iCAFs, myCAFs, basal-like tumor cells, and classical
tumor cells was performed. Strikingly, the nerve regions were
surrounded by regions with high scores of iCAFs and basal-like
signatures, whereas regions far away from the nerve were enriched with
high scores of myCAFs and classical signatures (Fig. [175]3d and
Supplementary Data [176]2). Furthermore, the perineural region was
divided into four tiers according to the distance to the nerve
(Fig. [177]3e and Supplementary Fig. [178]S3g), and quantitative set
analysis of gene expression (QuSAGE) was performed in each
tier^[179]45. We found that iCAFs and basal-like signatures
progressively increased from distant to proximal regions of the nerve,
whereas myCAFs and classical signatures decreased (Fig. [180]3f, g).
The heatmap showed that a similar change of marker genes for iCAF,
myCAF, basal-like tumor cell, and classical tumor cell signatures
(Fig. [181]3g and Supplementary Fig. [182]S3h). Similar results were
obtained using the reported markers of CAFs and tumor cells in previous
studies^[183]6,[184]46 (Supplementary Fig. [185]S3i). Collectively, the
subtypes of CAFs and tumor cells adjacent to Schwann cells were
characterized.
Schwann cells promote malignant progression in PDAC via midkine (MDK)
ST results showed that the basal-like signature was upregulated in the
surrounding Schwann cells (Fig. [186]3f, g and Supplementary
Fig. [187]S3h, i). It was widely recognized that basal-like cells are
the highly aggressive subtype in PDAC. To explore whether Schwann cells
facilitate PDAC cells gain malignant phenotypes, Schwann
cell-conditional medium (SC-CM) of two cell lines (RSC96 and sNF96.2)
was used to culture PDAC cells. Consistent with a previous
study^[188]28, the migration and invasion abilities of PDAC cells were
enhanced by SC-CM incubation (Fig. [189]4a–d). Elevated expression of
EMT markers was also observed after SC-CM treatment (Fig. [190]4e, f).
Similar results were observed for cell proliferation and the cell cycle
(Fig. [191]4g–j and Supplementary Fig. [192]S4a). As expected, GSEA
indicated that EMT and cell cycle pathways were enriched in groups
treated with SC-CM compared to controls (Supplementary Fig. [193]S4b).
Consistent with our scRNA-seq data, upregulation of basal-like-related
signatures and downregulation of classical-related signatures were also
shown in the CM treatment group via GSEA (Supplementary Fig. [194]S4c).
The gene set upregulated by SC-CM was defined as the SC-CM-associated
signature, which exhibited a similar trend to basal-like signatures
according to the distance to the nerve in ST data (Figs. [195]3g,
[196]4k and Supplementary Table [197]S1).
Fig. 4. Schwann cells promote malignant progression in PDAC via MDK.
[198]Fig. 4
[199]Open in a new tab
a–d Effects of RSC96 CM (a, c) and sNF96.2 CM (b, d) on migration (a,
b) and invasion (c, d) of Panc-1 and CFPAC-1 cells were assessed by
Transwell and Matrigel invasion assays. n = 10, 15, 16 (a) or 5 (b–d)
representative pictures over three independent experiments. Scale bar,
200 µm. e, f The relative expression levels of EMT markers in Panc-1
and CFPAC-1 cells cultured with RSC96 CM were detected by qPCR (e) and
western blotting (f). Data were representative of n = 3 independent
experiments. g–j Effects of RSC96 CM and sNF96.2 CM on the cell
proliferation of Panc-1 and CFPAC-1 cells were assessed by CCK-8 (g,
h), flow cytometry (i), and EdU assays (j). Scale bar, 100 µm. k QuSAGE
scores of control and SC-CM associated signature in perineural tier 1‒4
and other regions in the neuro-stroma niche. The signatures were based
on the bulk RNA-seq of CFPAC-1. l, m Effects of recombinant human MDK
on migration (l) and proliferation (m) of Panc-1 cells were assessed by
Transwell (l) and CCK-8 assay (m). Scale bar, 200 µm. n, o. Effects of
MDK neutralization antibodies on migration (n) and proliferation (o) of
Panc-1 cells cultured with RSC96 CM were assessed by Transwell (n) and
CCK-8 (o) assays. Scale bar, 200 µm. e, g–j, m, n Data were the
mean ± s.d. of n = 3 independent experiments. l, o Data were the
mean ± s.d. of n = 5 independent experiments. Statistical analysis:
unpaired two-sided t-test (a–e, i, j, l, n); two-way ANOVA (g, h, m,
o). Source data are provided as a Source Data file.
Mass spectrometry (MS) was performed to identify the key components of
SC-CM in promoting malignant progression. Of note, among the ligands
identified in SC-CM of all used cell lines, MDK aroused our interest as
it is a neurite growth-promoting factor proposed to mediate metastasis
in several cancer types (Supplementary Fig. S[200]4d and Supplementary
Data [201]3)^[202]47–[203]49. Recombinant MDK was used to treat PDAC
cells to assess the effects of MDK on tumor progression. Similar to
SC-CM treatment, MDK treatment also significantly enhanced the cell
proliferation and migration capacities of PDAC cells (Fig.[204]4l, m).
Moreover, the pro-tumor functional role of Schwann cells could be
greatly compromised by neutralizing antibodies against MDK (Fig.[205]
4n, o). Thus, these results demonstrated that Schwann cells promote the
malignant progression of PDAC via MDK.
Schwann cells induce a phenotypic switch in CAFs via interleukin-1 alpha
(IL-1α)
Cancer progression is recognized as a result of evolving crosstalk
between cancer cells and the surrounding CAFs, especially in PDAC,
which is characterized by prominent cancer-associated
desmoplasia^[206]2,[207]50. In addition to basal-like tumor cells,
iCAFs were also observed adjacent to Schwann cells in the ST data
(Fig. [208]3f, g and Supplementary Fig. [209]S3h, i). Moreover,
bioinformatics analysis of bulk RNA-seq data from the TCGA cohort
revealed that Schwann cell signature expression exhibited significant
enrichment of the iCAF-related signature (Fig. [210]5a and
Supplementary Fig. [211]S5a). We first performed multiple IF staining
to verify the spatial distribution of iCAFs and myCAFs in PDAC tumor
tissues. The results revealed that perineural IL-6^high cells (a marker
of iCAFs^[212]43) were adjacent to Schwann cells, whereas α-SMA ^high
cells (a marker of myCAFs^[213]43) were located farther away from the
neuro-stroma niche (Fig. [214]5b–g and Supplementary Fig. [215]S5b).
The average distance of iCAFs/myCAFs from the nerves turns to be 18.76
and 52.26 μm, respectively (Fig. [216]5h). We further demonstrated the
spatial distribution of iCAFs/myCAFs/nerves in clinical PDAC samples
(n = 31) using IHC staining. We also detected the perineural iCAFs and
EMT cancer cells enrichment, consistently (Fig. [217]5i–k and
Supplementary Fig. [218]S5c–h). Higher perineural iCAF numbers
correlated with a more aggressive tumor stage and worse patient outcome
(Fig. [219]5l–n). Thus, we hypothesized that Schwann cells might shape
the surrounding CAFs and induce the switch of phenotypes, which further
lead to PDAC progression.
Fig. 5. Perineural iCAFs widely exist in PDAC.
[220]Fig. 5
[221]Open in a new tab
a GSEA plot showing that the iCAF signature and IL6_JAK_STAT3 pathway
were enriched in PDAC samples with high expression levels of Schwann
cell signature. b Representative images of IF with anti-IL-6 (a marker
for iCAFs) and S100 β/p75NRT (a marker for Schwann cells) in the
neuro-stroma niche. Scale bar, 100 µm. c, d Quantification of IL-6
intensity in the regions of 0 to 40 μm or 40 to 80 μm away from Schwann
cells in representative panel b (c) or mean intensity of five
independent PDAC patients (d). e Representative images of IF with
anti-α-SMA (a marker for myCAFs) and S100 β/p75NRT in the neuro-stroma
niche. Scale bar, 100 µm. b, e Images are representative of five PDAC
samples with similar results. f, g Quantification of α-SMA intensity in
representative panel e (f) or mean intensity of five independent PDAC
patients (g). h Quantification of the average distance of iCAFs or
myCAFs to Schwann cells. n = 50 cells. i Representative images of IHC
staining with anti-IL-6 and α-SMA in human PDAC. Scale bar, 50 µm.
Images are representative of 31 PDAC samples with similar results. j
Quantification of the iCAFs (IL-6^high/α-SMA^Low) number 0 to 20 or 20
to 40 μm away from Schwann cells in 31 PDAC patients. k Quantification
of the iCAFs or myCAFs (IL-6^Low/α-SMA^High) number in the region of 0
to 20 μm away from Schwann cells in 31 PDAC patients. l Violin chart of
iCAFs number of PDAC with AJCC stage (IIa, n = 8; IIb, n = 8; III/IV
n = 15) in 31 PDAC patients. m Violin chart of iCAFs number of PDAC
with N stage (N0, n = 11; N1, n = 11; N2, n = 9) in 31 PDAC patients.
c, f, h, j, k, l, m For each violin, the minimum, first quartile,
median, third quartile, and maximum were displayed. n OS analysis of
PDAC patients with low perineural iCAF number (<8; n = 24) or high (>9;
n = 7) in PDAC patients (n = 31). Cutoff of the iCAF number was
determined by X-tile. HR and 95% CI were determined using the
regression coefficient of the Cox model. Statistical analysis: unpaired
two-sided t-test (c, f, h, j–m); paired two-sided t-test (d, g);
log-rank test (n). Source data are provided as a Source Data file.
To further characterize the role of Schwann cells in CAFs, SC-CM was
applied to fresh PDAC tissues derived CAFs (CAF-1 and CAF-2), which
were then cultured as primary cells and validated using western
blotting and IF (Supplementary Fig.S[222]6a, b). SC-CM incubation
promoted the proliferation of CAFs, as the percentage of cells in the S
phase was significantly increased, and the apoptotic index was markedly
decreased in the treatment group compared to the control group
(Supplementary Fig. [223]S6c–f). Gene ontology (GO) and Kyoto
Encyclopedia of Genes and Genomes (KEGG) analyses of RNA-seq for CAFs
showed that the pathways of cytokines, cell cycle, and nerve system
development were enriched in CAFs after SC-CM incubation (Supplementary
Fig. [224]S6g). Next, whether Schwann cells induced the switch of CAF
to iCAF phenotype was studied; SC-CM incubation resulted in the
upregulation of iCAF markers (IL-6, LIF, PDGFRα, IL1R1, and CXCL1) and
the downregulation of myCAF markers (α-SMA and CTGF) in CAFs
(Fig.[225]6a–d and Supplementary Fig. S[226]7a–d). Similar results were
observed with the co-culture of CAFs and Schwann cells (Supplementary
Fig. [227]S7e). Consistent with our scRNA-seq results, SC-CM treatment
could also switch PSCs, the major source of CAFs in PDAC, to the iCAF
phenotype (Supplementary Fig. [228]S7f–h). Interestingly, SC-CM
incubation upregulated the pathways of cytokine-cytokine receptor
interaction and neuroactive ligand–receptor interaction in CAFs, which
was an echo of the features of iCAFs unveiled in scRNA-seq
(Supplementary Fig. [229]S7i). As expected, the SC-CM-associated CAF
signature displayed a similar trend in the iCAF signature according to
the distance to the nerve (Figs. [230]3g, [231]6e and Supplementary
Table [232]S1).
Fig. 6. Schwann cells induce a phenotypic switch in CAFs via IL-1α.
[233]Fig. 6
[234]Open in a new tab
a The relative mRNA levels of iCAF and myCAF markers in CAF-2 cultured
with RSC96 CM and control. b The protein level of α-SMA in CAF-2
cultured with 0, 20, or 100 μg/ml RSC96 CM. c The fold change of IL-6
in CAF-2 cultured with RSC96 CM for 0, 1, 6, or 24 h was evaluated by
ELISA. d Representative images and quantification of IF with anti-α-SMA
in CAF-2 cultured with RSC96 CM and control. Scale bar, 100 µm. Images
are representative of three biologically independent experiments with
CAF-2 cells with similar results. e QuSAGE scores of control and SC-CM
associated signature in perineural tier 1–4 and other regions in the
neuro-stroma niche. The signatures were defined by the bulk RNA-seq of
CAFs with SC-CM treatment. f Schematic diagram of ex vivo assay. g The
relative mRNA levels of iCAF and myCAF markers in PDAC tissue cultured
with RSC96 CM and control. h, i Representative images (h) and
quantification (i) of IF with anti-α-SMA and IL-6 in PDAC tissue
cultured with RSC96 CM and control. Scale bar, 20 µm. j Representative
images and quantification of IL-1α in PDAC tissues. Scale bar, 20 µm.
h, j Images are representative of five PDAC samples with similar
results. k The relative mRNA levels of iCAF and myCAF markers in CAF-2
treated with IL-1α or RSC96 CM. l The relative mRNA levels of iCAF and
myCAF markers in CAF-2 cultured by RSC96 CM pretreated with
neutralization antibodies against IL-1α or IgG control. m Schematic
diagram of how Schwann cells shape tumor cells and CAFs in the
neuro-stroma niche. n Schematic diagram of the in vivo orthotopic
injection. A cell mixture of CAFs, CFPAC-1, and sNF96.2 cells was
injected into mice pancreas, then the mice subjected to MDK inhibitor
(iMDK) and interleukin-1 receptor antagonist (IL-1RA) treatment (INHs,
inhibitors) or not (n = 5). o–q Photograph (o) and quantification (p,
q) of orthotopic tumors of mice. a, c, d, g, k. l Data were the
mean ± s.d. of n = 3 independent experiments. i, p, q Data were the
mean ± s.d. of n = 5 independent experiments. f, m, n Created with
BioRender.com. Statistical analysis: unpaired two-sided t-test (a, c,
d, g, i, k, l, p, q); paired two-sided t-test (j). Source data are
provided as a Source Data file.
To further recapitulate the reversible features of CAFs in primary PDAC
tissues, a three-dimensional ex vivo culture model of tumor tissues was
established (Fig. [235]6f and Supplementary Fig. [236]S7j). Fresh PDAC
tissues were dissected into approximately 1 mm^3 pieces and then
submerged in a culture medium with or without SC-CM^[237]51. Increased
levels of inflammatory cytokines and loss of myCAF features were
observed in ex vivo tumor tissues (Fig. [238]6g–i and Supplementary
Fig. S[239]7k, l). Moreover, GESA of RNA-seq results showed that SC-CM
incubation induced the upregulation of iCAF-related signatures while
decreasing the signatures of myCAFs (Supplementary Fig. S[240]7m).
Thus, Schwann cells could trigger CAFs to switch to the iCAFs
phenotype. IL-1α is the major ligand responsible for iCAF formation in
PDAC^[241]52. It was intriguing to find that Schwann cells were an
important source of IL-1α (Fig. [242]6j and Supplementary Fig.
S[243]8a–d). Similar to SC-CM, recombinant IL-1α upregulated the
markers of iCAFs, whereas neutralizing antibodies against IL-1α
abolished the effects of Schwann cells on iCAF phenotypes
(Fig. [244]6k, l and Supplementary Fig. S[245]8e–g). Together, our data
revealed a malignant niche of PDAC, in which Schwann cells shape the
heterogeneity of CAFs and cancer cells. Schwann cells enhance
basal-like features in PDAC cells via MDK. Besides, Schwann cells
induce the switch of CAFs to iCAFs by secreting IL-1α. (Fig. [246]6m).
To further validate the therapeutic potential of nerve/stroma/tumor
niches, an orthotopic Schwann cells-containing tumor was induced by
co-injection of PDAC cells, CAFs, and Schwann cells to mice pancreas
(Fig. [247]6n). In consistent, MDK inhibitor (iMDK) and Interleukin-1
receptor antagonist (IL-1RA) substantially suppressed PDAC progression
(Fig. [248]6n–q and Supplementary Fig. [249]S8h).
Schwann cells hijack iCAFs to accelerate cancer progression
Co-culture of tumor organoids and iCAFs leads to a marked upregulation
of EMT markers and iCAFs are associated with a poor
prognosis^[250]43,[251]50,[252]52,[253]53. To investigate whether iCAFs
induced by Schwann cells contribute to tumor progression, in vitro and
in vivo functional assays of PDAC cells were performed.
With SC-CM treatment or Transwell co-culture with Schwann cells in
vitro, CAFs expressed higher levels of inflammation-related genes
(Fig. [254]6a and Supplementary Fig. [255]S7d, e). Thereafter, the
culture medium of SC/CAF alone/SC-induced CAFs was used to culture PDAC
cells. The culture medium from Schwann cells or CAFs alone could
enhance the migration abilities of PDAC cells (Fig. [256]7a and
Supplementary Fig. [257]S9a). More importantly, the culture medium of
induced iCAFs by co-culture with Schwann cells or SC-CM facilitated
PDAC cells to acquire strong capacities for migration (Fig. [258]7b–d
and Supplementary Fig. [259]S9b, c).
Fig. 7. Schwann cells hijack iCAFs to accelerate cancer progression.
[260]Fig. 7
[261]Open in a new tab
a‒c The migration capabilities of Panc-1 cells were enhanced by the CM
of activated CAFs. CAFs were directly co-cultured (a) or Transwell
co-cultured with (b) Schwann cells, or cultured with Schwann cells CM
(c), respectively. Direct co-culture (a): CM of CAFs, RSC96, and cell
mixture of CAFs and RSC96 were harvested. Transwell co-culture (b):
CAFs and RSC96 cells were co-cultured via Transwell chambers for 48 h,
and CAFs was washed by PBS three times, then CAFs were cultured with
fresh medium for another 48 h, at last, CAFs CM was harvested. Schwann
cell CM (c): CAFs was cultured with Schwann cell CM for 48 h and the
removal of SC-CM was followed by three rinses with PBS, and then CAFs
were cultured with fresh medium for another 48 h, at last, CAFs CM was
harvested. After 48 h culture with CAFs CM, Panc-1 cells were harvested
for migration assay. d The protein levels of E-cadherin, Vimentin,
Fibronectin, and β-catenin in Panc-1 cells treated with CM of CAFs,
which were cultured with or without (w/ or w/o) RSC96 CM. Data were
representative of n = 3 independent experiments. e Schematic diagram of
the in vivo orthotopic injection. A cell mixture of CAFs (w/ or w/o
RSC96 CM/IL-1RA treatment) and tumor cells was injected into mice
pancreas (n = 5). f, g Bioluminescence photograph (f) and
quantification (g) of orthotopic tumors of mice injected with a cell
mixture of Panc-1 cells and CAFs. h Schematic diagram of in vivo liver
metastatic model. Panc-1 (i, j) or CFPAC-1(k, l) were pretreated with
ctrl medium (G1), SC-CM(G2), SC-CM+iMDK(G3), ctrl-CAF-CM(G4),
SC-induced CAF-CM(G5), or SC + IL-1RA-induced CAF-CM (G6), and then
injected via spleen (n = 5). i–l Representative photograph (i, k) and
quantification (j, l) of Panc-1 (i, j) or CFPAC-1 (k, l) liver
metastatic tumors. a, c (right), Data were the mean ± s.d. of n = 4
independent experiments. b, c (left), g, j, l Data were the mean ± s.d.
of n = 5 independent experiments. a–c, e, h Created with BioRender.com.
Statistical analysis: unpaired two-sided t-test (a–c, j, l); two-way
ANOVA (g). Source data are provided as a Source Data file.
Using a patient-derived xenograft (PDX) mouse model, we found that with
SC-CM treatment, patient-derived tumor tissues not only formed larger
subcutaneous tumors in nude mice but also resulted in a higher
incidence of tumor formation (25 vs. 16.7%, n = 12) (Supplementary
Fig. [262]S9d–g).
To assess the effect of neuro-stroma milieu on PDAC progression in
vivo, PDAC cells mixed with SC-CM/IL-1RA-pretreated CAFs or control
CAFs were injected into the pancreases (Fig. [263]7e). We found that
SC-CM activated CAFs significantly increased PDAC progression in vivo
(Fig. [264]7e–g and Supplementary Fig. [265]S9h–l, G2 vs G1), and
IL-1RA treatment substantially suppressed tumor sizes (Fig. [266]7e–g
and Supplementary Fig. [267]S9h–l, G3 vs G2).
To investigate this effect of SC-CM/activated CAF on tumor metastasis,
PDAC cells pretreated by SC-CM/CAF-CM/inhibitors were injected into the
spleen (Fig. [268]7h). Larger metastatic tumor and higher metastatic
frequency were observed in the group with SC-CM or SC-induced CAF-CM
treatment (Fig. [269]7i–l and Supplementary Fig. [270]S9m, n, G2 vs G1;
G5 vs G4; Supplementary Fig. [271]S9o–s, IL-1α (G3) was used as a
positive control). Consistently, iMDK could rescue the SC-CM-induced
tumor metastasis, and IL-1RA could abolish malignant CAF phenotype
induced by SC-CM (Fig. [272]7i–l and Supplementary Fig. [273]S9m, n, G3
vs G2; G6 vs G5; Supplementary Fig. [274]S9o–t, IL-1RA without SC-CM
(G4) or CAF-CM (G5) were used as negative controls).
Collectively, we could also conclude that Schwann cells promote PDAC
progression in vivo by shaping the phenotypes of tumor cells and CAF,
which was induced by MDK and IL-1, respectively.
Discussion
PDAC is characterized by extensive stroma and prominent neural
alterations. The neural niche in the TME provides various cellular
components and soluble molecules that facilitate cancer cell survival,
proliferation, invasion, and motility^[275]54. Increasing evidence
highlighted the critical role of nerve, especially Schwann cells, in
PDAC development and progression^[276]21,[277]30. The lack of a
reliable modeling system has resulted in a paucity of studies on neural
niches in the TME. Fortunately, a large number of PDAC tumor tissues,
together with adjacent non-tumor tissues in our center (Ruijin
Hospital, Shanghai Jiao Tong University School of Medicine, China),
were used to investigate the clinical significance of Schwann cells in
the TME. Similar to previous studies, we demonstrate that accumulation
of Schwann cells in tumor tissues are associated with poor
prognosis^[278]9,[279]30,[280]55.
Interestingly, Schwann cells are surrounded by stroma and tumor cells
in the neuro-stroma niche of the TME (Fig. [281]1f). Schwann cells and
other nerve cells are relatively rare in the TME compared with stromal
and tumor cells; therefore, scRNA-seq and ST were applied to PDAC
tissues with neural hypertrophy. Not surprisingly, only a very small
population of Schwann-like cells cluster was detected in scRNA-seq,
which was consistent with the previous findings^[282]32,[283]33.
However, scRNA-seq revealed the heterogeneity of TME cells in the
context of neural hypertrophy, including basal-like and classical tumor
cells, stellate-like cells, myCAF, and iCAFs. Basal-like subtype tumors
not only exhibit faster growth rates but also enhanced migration and
invasion capabilities compared to classical tumors. Moreover,
basal-like subtype tumors have a poor
prognosis^[284]40,[285]41,[286]46,[287]56. As a type of pericyte
primarily located at the base of the acini and around vascular,
stellate-like cells in our study also express some markers of other
mesenchymal cells, e.g., pericytes/vascular smooth muscle cells in
breast cancer^[288]57. Currently, the origin of CAFs is still
controversial. Although it was widely reported that pancreatic stellate
cells (PSCs) were the major source of CAFs in PDAC^[289]43, Helms et
al. reported that only 10–15% of CAFs originated from Fabp-GFP marker
PSCs in mice model by transplanting tumor-stroma^[290]58. Cell fate
trajectory analyses in our study suggested that stellate cells were
precursors of the two main PDAC CAF subsets, myCAFs and iCAFs
(Fig. [291]2j). Further experiments in molecular biology are required
to validate the hypothesis. In PDAC, iCAFs and myCAFs coexist as
mutually exclusive and reversible subtypes^[292]52. iCAFs are
characterized by their cytokine-secreting properties. In tumor
organoid-conditioned media, CAFs undergo a specific loss of
myofibroblastic features but acquire phenotypes, including
morphological activation, enhanced proliferation, and upregulated IL-6,
IL-11, and leukemia inhibitory factor (LIF)^[293]43. iCAFs are
potential mediators of immune suppression and play a role in promoting
cancer progression^[294]6,[295]59–[296]63. scRNA-seq indicated that the
nerve development and neural plasticity pathways were enriched in both
basal-like tumor cells and iCAFs, suggesting an interplay between
basal-like tumor cells, iCAFs, and nerves.
ST provides an unbiased and comprehensive picture of spatial
composition to generate tissue atlases and have been adopted in a range
of development, physiological and disease context, including
cancers^[297]64. ST analysis indicates that iCAFs were co-localized
with cancer cells expressing the stress-response gene module^[298]65.
In our study, ST analysis showed that basal-like and iCAF-related
signatures were enriched in the regions of Schwann cells, whereas the
regions exhibiting classical- and myCAF-related signature were farther
away from Schwann cells, suggesting that Schwann cells may induce the
switch of tumor cells and CAFs to basal-like cells or iCAFs,
respectively.
As the most prevalent cell type in peripheral nerves, Schwann cells are
activated during tumor progression, even during the preneoplastic stage
of PDAC^[299]17. They are detectable around PanIN lesions in
genetically engineered PDAC mouse models^[300]17. During PDAC
development and progression, Schwann cells work as paths of axonal
guidance toward tumor cells, and lead to “neurogenesis” in TME, which
is supported by histopathological manifestations with increased neural
density and hypertrophy in tumor tissues^[301]55,[302]66. Human and
transgenic mouse PDAC harbor abundant Schwann cells, which are widely
dispersed in the tumor-stroma^[303]17. The ST results indicated that
the subtypes of tumor cells and CAFs adjacent to Schwann cells were
distinct from those farther away. Schwann cells may contribute to the
remodeling of the neuro-stroma niche in PDAC tissues.
Schwann cells have recently attracted extensive attention. Both
co-culture and the conditioned medium of Schwann cells increase the
migratory and invasive abilities of PDAC cells^[304]27,[305]29.
Moreover, Schwann cells enhance prostate and pancreatic cancer cell
invasion via laminin-binding A6 integrin^[306]67 and promote EMT and
invasion abilities of lung cancer cells through the
CXCL5/CXCR2/PI3K/AKT/GSK-3beta/Snail-Twist pathway^[307]26. Consistent
with these findings^[308]26,[309]27,[310]29,[311]67, we also
demonstrate that Schwann cells could promote the tumorigenicity of PDAC
cells using in vivo and in vitro functional assays. More importantly,
the co-culture of Schwann cells and CAFs can induce the switch of CAFs
to pro-tumor iCAFs, which accelerates the progression of PDAC. The
current study focuses on MDK and IL-1α as two mediators of Schwann
cells in tumor cells and CAFs. Pharmacological inhibition of MDK and
IL-1R antagonist could substantially abolish the Schwann cells induced
PDAC progression in vivo. Other neurotrophic factors or chemokines may
play similar roles in the neuro-stroma niche. However, the mechanism of
the inclination of Schwann cells to migrate toward tumors and the
effects on initiation of the neuro-stroma niche remain to be
elucidated. As an important participant in tumor progression, IL-1α
could also have multiple source and effector cells in TME^[312]68.
In summary, the current research reveals the heterogenicity of tumor
cells and CAFs in the neuro-stroma niche by an integrated analysis of
RNA sequencing at the single-cell level, spatial gene expression
profiling, and histology. The study further demonstrates that Schwann
cells can induce malignant progression of tumor cells and CAFs by
releasing MDK and IL-1α. In the future, it will be important to explore
the roles of more key factors, such as glial cell-derived neurotrophic
factors, neural growth factors, and chemokines, in shaping the
neuro-stroma niche. We hope that our study will provide a strong
rationale for considering the effects of Schwann cells on shaping the
main cells in the TME, and shed light on personalized therapy targeting
Schwann cells.
Methods
This research complies with all relevant ethical regulations. All
experiments were reviewed and approved by Ruijin Hospital.
Human tissue samples and tissue microarrays
Human pancreatic ductal adenocarcinoma (PDAC) resection specimens were
obtained from patients who underwent pancreatectomy at Ruijin Hospital
(Shanghai, China) between January 2016 and December 2018. The sample
collection and preparation protocol were approved by the Ruijin
Hospital Ethics Committee (reference number: 2013-70). All samples were
obtained with written informed consent and were fully anonymized.
Consent to publish relevant clinical information potentially
identifying individuals (e.g., age, gender, histological grade, etc.)
was obtained. This research was conducted according to the principles
of the Declaration of Helsinki.
All patients were enrolled if: (1) the histopathological diagnosis with
PDAC; (2) no prior anti-cancer treatments; (3) no other malignant
history; (4) follow-up was completed within the scheduled time frame.
Patients with infectious diseases, rheumatic diseases, or other
malignancies were excluded. The samples were confirmed based on the two
pathologists’ assessments. A total of 187 pairs of normal pancreases
and PDAC were obtained from surgical specimens for tissue microarrays,
including 112 males and 75 females with median ages of 62 and 67 years
old.
H&E, IHC, and IF
H&E, IHC, and IF staining were performed in PDAC tissues from archived
formalin-fixed paraffin-embedded (FFPE) tumors and were performed
according to standard protocols. For IHC, the slides were incubated
with PGP9.5 (Servicebio, GB11159, 1:500), p75NRT (Abcam, ab52987,
1:100), GFAP (Proteintech, 16825, 1:200), S100β (Servicebio, GB11359,
1:150), IL-6 (Abcam, ab9324, 1:50), α-SMA (Abcam, ab5694, 1:200),
N-Cadherin (Proteintech, 22018-1-AP, 1:100), Fibronectin (Proteintech,
15613-1-ap, 1:200), FAP (Cell signaling technology [CST], 66562,1:100)
followed by HRP conjugated Goat Anti-Mouse IgG (H + L) (Servicebio,
GB23301, 1:200) or HRP conjugated Goat Anti-Rabbit IgG (H + L)
(Servicebio, GB23303, 1:200) and diaminobenzidine (Brown) (DAB, Dako).
The slides were counterstained with hematoxylin (Dako), which stains
nuclei blue, contrasting with the brown of HRP-DAB. Fibroblast
morphology was confirmed based on the two pathologists’ assessments. IF
was performed according to the manufacturer’s instructions (Panovue,
10004100100). Briefly, slides were incubated with primary antibodies
for S100β (Servicebio, GB11359, 1:500), p75NRT (Abcam, ab52987, 1:100),
α-SMA (Abcam, ab5694, 1:200), IL-6 (Abcam, ab233706, 1:50), and IL-1α
(Proteintech, 16765-1-AP, 1:50) overnight at 4 °C, and stained with
HRP-labeled goat anti-mouse/rabbit IgG secondary antibodies.
scRNA-seq
The BD Rhapsody system was used to capture transcriptome information.
Quantified by a High-Sensitivity DNA chip (Agilent) on a Bioanalyzer
2200 and Qubit High-Sensitivity DNA analysis (Thermo Fisher
Scientific), sequencing was performed using HiSeqXten (Illumina, San
Diego, CA, USA) with a 150 bp paired-end run. scRNA-seq was performed
on single-cell suspensions with viability >70%. Single-cell capture was
achieved by randomly distributing a single-cell suspension in >200,000
microwells using the limiting dilution method. To pair the beads with
cells, beads with oligonucleotide barcodes were added to the saturation
state in the microwell. Cells were lysed in the micropores, allowing
polyadenylated RNA molecules to hybridize into the beads. The beads
were collected and followed by reverse transcription and ExoI
digestion. During cDNA synthesis, each cDNA molecule was marked with a
unique molecular identifier (UMI) at the 5’ end (that is, the 3’ end of
the mRNA transcript), and a cell barcode was used to mark its cellular
origin. The BD Rhapsody Single Cell Whole Transcriptome Amplification
(WTA) workflow was used to prepare the entire transcriptome library,
which included random primer and extension (RPE), RPE amplification
polymerase chain reaction (PCR), and WTA index PCR.
scRNA-seq statistical analysis
Data analysis of scRNA-seq was performed by NovelBio Bio-Pharm
Technology Co., Ltd. with the NovelBrain Cloud Analysis Platform. The
adapter sequence was filtered using Fastp with the default parameter,
and then low-quality reads are removed. To identify the cell barcode
whitelist, we used UMI tools for analysis. Mapped to the human genome
(Ensemble version 91) using STAR mapping with customized parameters
from the UMI-tools standard pipeline, the UMI counts of each sample
were obtained. Cells containing over 200 expressed genes and
mitochondrial UMI rates below 40% passed the cell quality filtering,
and mitochondria genes were removed. The Seurat package (version:
3.1.4, [313]https://satijalab.org/seurat/) was used for cell
normalization and regression based on the expression table according to
the UMI counts of each sample and the percentage of mitochondria to
obtain scaled data.
Utilizing the graph-based cluster method (resolution = 0.8), we
acquired the unsupervised cell cluster result based on the principal
component analysis (PCA) top ten principal components, and we
calculated the marker genes using the FindAllMarkers function with the
Wilcoxon rank-sum test algorithm under the following criteria:(1) lnFC
>0.25; (2) P value <0.05; and (3) min.pct >0.1. To identify the cell
type in detail, clusters of the same cell type were selected for
re-t-SNE analysis, graph-based clustering, and marker analysis, and
single-cell data were further processed using only clusters identified
as malignant and fibroblasts. Re-clustering was performed on malignant
cells and fibroblasts using a method similar to that described
previously^[314]69.
GO and KEGG analysis was performed to elucidate the biological
implications of marker genes and differentially expressed
genes^[315]70. GO annotations were downloaded from NCBI
([316]http://www.ncbi.nlm.nih.gov/), UniProt
([317]http://www.uniprot.org/), GO ([318]http://www.geneontology.org/),
and KEGG ([319]https://www.genome.jp/kegg/). Fisher’s exact test was
applied to identify significant GO and KEGG categories, and FDR was
used to correct P values.
CytoTRACE Analysis: We applied CytoTRACE Analysis for cell development
analysis with default parameters, and the CytoTRACE score of each cell
was calculated based on the expression matrix^[320]39.
TCGA Analysis: The same type of cancer data as clinical information can
be downloaded from The Cancer Genome Atlas
([321]http://cancergenome.nih.gov). The Kaplan–Meier test was used to
analyze the gene set relationships. The threshold of significance was
defined by the P value.
Pseudo-time analysis: We applied single-cell trajectory analysis
utilizing Monocle2
([322]http://cole-trapnell-lab.github.io/monocle-release) using
DDR-Tree and default parameters. Before Monocle analysis, we selected
marker genes from the Seurat clustering results and raw expression
counts of the filtered cells. Based on pseudotime analysis, branch
expression analysis modeling (BEAM analysis) was applied for branch
fate-determined gene analysis.
Microarray-based ST
Tissue permeabilization and spatial transcriptomic sequencing were
performed using Visium Spatial Gene Expression Slides & Reagent Kits.
Quantified by the qubit high-sensitivity DNA assay (Thermo Fisher
Scientific), the size distribution of the final libraries was further
determined using a high-sensitivity DNA chip on a Bioanalyzer 2200
(Agilent). All libraries were sequenced using an Illumina sequencer
(Illumina) with a 150 bp paired-end run. After surgically removed, PDAC
tumor tissues were embedded in the optimal cutting temperature (OCT)
compound (Sakura). Further, the tissues with OCT compound were
immediately frozen using dry ice. Tissue sections (10 μm thick) were
placed within the frames of capture areas on Visium Spatial slides (10X
Genomics) and imaged using a Pannoramic MIDI microscope (3DHISTECH)
after H&E staining. To optimize permeabilization time, we performed
pre-permeabilization using Visium spatial tissue optimization slides
and reagent kits (10X Genomics). For tissue permeabilization and
spatial transcriptomic sequencing, we first performed the reverse
transcription by incubating the stained slides in RT Master Mix at
53 °C for 45 min after permeabilization. To initiate second strand
synthesis, the tissue sections were incubated for 15 min at 65 °C with
a Second Strand mix added to the slides. After transferring the cDNA,
it was purified via the barcode cDNA and amplified. The amplified
barcoded cDNA was fragmented, A-tailed, ligated with adapters, and
amplified using PCR.
ST statistical analysis
We applied fastp with default parameter filtering of the adapter
sequence and removed low-quality reads to achieve clean data^[323]71.
Then, feature-barcode matrices were obtained by aligning reads to the
human genome (GRCh38) using SpaceRanger v1.1.0. To minimize the sample
batch, we applied downsample analysis among samples sequenced according
to the mapped barcoded reads per spot of each sample, and finally
achieved the aggregated matrix.
The Seurat package (version 3.2, [324]https://satijalab.org/seurat/)
was used for spot normalization and regression. The fastMNN function
from the R package scran (v1.10.2) was used to apply the mutual nearest
neighbor method to correct for batch effects among samples^[325]72. PCA
was constructed based on scaled data with all highly variable genes,
and the top 30 principals were used for UMAP construction. Utilizing
the graph-based cluster method, we acquired the unsupervised cell
cluster result based on the PCA top 30 principal components, and we
calculated the marker genes using the FindAllMarkers function with the
Wilcoxon rank-sum test algorithm under the following criteria:(1) lnFC
>0.25; (2) P value <0.05; and (3) min.pct >0.1. Spatial feature
expression plots were generated using the SpatialFeaturePlot function
in Seurat (version 3.1.3) and STUtility R package (version 1.0.0).
ssGSEA: We applied gene set enrichment analysis based on single
cell-specific gene sets and normalized gene expression matrix by ssGSEA
function in the GSVA package to achieve the gene enrichment score of
each spot^[326]73.
GO analysis was performed similarly to scRNA-seq.
QuSAGE Analysis: To characterize the relative activation of a given
gene set, such as pathway activation, we performed a QuSAGE (2.16.1)
analysis^[327]45.
Cell culture
All cell lines were maintained at 37 °C with 5% CO[2]. Panc-1, RSC96,
sNF96.2, CAFs, hPSCs, and NF cells were cultured in DMEM (Meilunbio,
China) containing 10% fetal bovine serum (FBS) (Gibco; Life Technology)
and 50 μg/mL penicillin/streptomycin (P/S). CFPAC-1 cells were cultured
in IMDM (BIOAGRIO) containing 10%FBS and 50 μg/mL P/S. Panc-1
(CRL-1469™), CFPAC-1 (CRL-1918™), RSC96 (CRL-2765™), and
sNF96.2(CRL-2884™) were purchased from ATCC. NF and CAFs were isolated
from human normal/tumor tissues. hPSC was a gift from Dr. Yuan Fang,
which were purchased from ScienCell Research Laboratories, Carlsbad, CA
(the HPaSteC cells, #3830). All cells were checked routinely for the
absence of mycoplasma contamination. Short tandem repeat profiling was
used to authenticate all the cell lines.
Fibroblasts isolation and analysis
NF and CAFs were isolated from human normal pancreases (NF)/PDAC
tissues (CAFs) as previously described in ref. ^[328]74 with some
modifications. Briefly, the samples were cut into small pieces and
collagenase was digested. The tissue was seeded in the culture dish
with 10% FBS/DMEM. After 7–10 days, fibroblasts started to grow out of
the tissue into the dish. Fibroblasts were confirmed by morphology and
expression of CAF markers, including α-SMA (Abcam, ab5694, 1:1000 and
1:200) and FAP (CST, 66562, 1:1000), by Western blotting and IF.
CM concentration
RSC96 and sNF96.2 cells were cultured in 10% FBS/DMEM until reaching
80–90% confluence. Thereafter, the medium was replaced with DMEM
without FBS or phenol red and cultured for 24 h. Then, the cell culture
medium was harvested and concentrated using an Amicon Ultra-4
centrifugal filter device (3000 MWCO cutoff) (Millipore) at 3000×g for
1 h. Protein concentrations were determined using the bicinchoninic
acid assay (Thermo Fisher Scientific). The concentrated CM was
collected and stored at −80 °C until further use.
Co-culture of Schwann cells, cancer cells, and CAFs in vitro
For incubation of tumor cells with SC-CM, cancer cells were incubated
with 100 µg/ml SC-CM for 72 h.
For direct co-culture, 1
[MATH: × :MATH]
10^5 CAFs and 1
[MATH: × :MATH]
10^5 RSC96 cells were mixed and seeded in six-well plates overnight.
About 2
[MATH: × :MATH]
10^5 CAFs or 2
[MATH: × :MATH]
10^5 RSC96 cells were cultured alone as controls. The cells were
cultured in 10%FBS/DMEM for 24–48 h, and the supernatant was harvested
using a 0.22 µm filter. For Transwell-based co-cultures, 2
[MATH: × :MATH]
10^5 CAFs were seeded in the lower compartment of six-well plates and
2
[MATH: × :MATH]
10^5 RSC96 were seeded on top of the Transwell membrane (Corning,
0.4 µm). After 48 h of co-culture, CAFs were washed by PBS three times,
and then an equal number of CAFs were cultured individually in fresh
medium for 24–48 h, and the supernatant was harvested using a 0.22 µm
filter. For the incubation of CAFs with SC-CM, CAFs were incubated with
20–100 µg/ml SC-CM for 48 h. After being rinsed by PBS for three times,
an equal number of CAFs were cultured in fresh medium for 24–48 h, and
CAFs supernatant was collected. CAFs CM were incubated with 1–10
[MATH: × :MATH]
10^5 Panc-1 or CFPAC-1 for an additional 48 h. Then cancer cells were
harvested for further study.
Ex vivo tissue culture
An ex vivo culture model of tumor tissue was established, as previously
described in ref. ^[329]51 with some modifications. Briefly, fresh PDAC
tissues were dissected into approximately 1 mm^3 pieces and then
submerged in 10% FBS/DMEM with 100 µg/ml RSC96 CM for 24‒48 h.
PDAC liver metastatic, orthotopic, and PDX models
All experiments performed on mice were reviewed and approved by the
review board on the use of living animals at Ruijin Hospital. Nude
BALB/c mice (6 weeks old) were purchased from the Chinese Academy of
Sciences (Shanghai, China) and maintained in a specific pathogen-free
facility. Panc-1 cells were labeled with firefly luciferase using an in
vivo imaging system (IVIS). Luciferin emission imaging of
isoflurane-anesthetized animals was performed using the IVIS Spectrum
(Tanon) after intraperitoneal injection of d-luciferin (150 mg/kg;
Promega, P1043) into mice. Tumor size was measured using a digital
caliper, and tumor volume was calculated as 0.5 × length × width^2. No
sex selection was performed in this study. Mice were euthanized before
the tumor volume exceeded 1500 mm^3.
The orthotopic implantation model was established as previously
reported^[330]75. Briefly, for the co-injection model in Fig. [331]6n,
2 × 10^6 luciferase-expressing Panc-1, 2 × 10^6 CAF-2 and 1 × 10^5
sNF96.2 cells were suspended in 25 μL PBS and injected into the body of
the pancreas. After 14 days, mice were divided into control or
inhibitors groups, the latter were treated with iMDK (9 mg/kg, MCE,
HY-110171A) and IL-1RA (25 mg/kg, MCE, AMG-719) by intraperitoneal
injection every 48 h.
For the co-injection model in Fig. [332]7e, 2 × 10^6 Panc-1 or CFPAC-1
and 2 × 10^6 CAF-2 (w/ or w/o RSC96 CM/IL-1RA (1 μg/ml) treatment) were
injected into the pancreas.
For the liver metastatic models in Fig. [333]7h, cells suspended in
30 μL PBS were injected into the spleen. About 2 × 10^6 Panc-1 or
CFPAC-1 cells were treated with ctrl CM(G1), SC-CM(G2), SC-CM+iMDK
(2.5 μM) (G3), normal CAF-CM(G4), SC-induced CAF-CM(G5), or SC + IL-1RA
(1 μg/ml) induced CAF-CM (G6) for 72 h before injection. For Sup Fig.
9o, 2 × 10^6 CFPAC-1 cells were treated with ctrl CM (G1), normal
CAF-CM (G2), recombined IL-1α-induced (10 ng/ml) CAF-CM (G3), IL-1RA
(1 μg/ml) treated CAF-CM (G4) or IL-1RA (1 μg/ml) only (G5) for 72 h
before injection.
For the PDX model, fresh PDAC tissues were dissected and washed twice
with PBS, trimmed into 1 mm^3 fragments, and finally submerged in 10%
FBS/DMEM with or without RSC96 CM (100 μg/ml) for 48 h. After ex vivo
culture, the tumor samples with Matrigel (Corning) were subcutaneously
transplanted into the dorsal flank of mice.
Bulk RNA sequencing
CFPAC-1, CAF-1, and CAF-2 were lysed with TRIzol (Invitrogen,
15596026), and total RNA was extracted using RNeasy Mini Kit (Qiagen,
74104), according to the manufacturer’s instructions. RNA sequencing
libraries were generated using the KAPA RNA library preparation kit
(Illumina) and the KAPA dual-indexed adapter kit (Illumina), and
sequenced on an Illumina HiSeq X10 platform (2 × 150 bp).
Mass spectrometry (MS)
MS was performed to validate candidate molecules in the CM of Schwann
cells. Briefly, RSC96 and sNF96.2 CM were denatured, alkylated, and
digested in situ with trypsin. Tryptic peptides were analyzed using an
Orbitrap Fusion LUMOS mass spectrometer (Thermo Fisher Scientific)
coupled to an Easy-nLC 1200 via an Easy Spray (Thermo Fisher
Scientific).
Cell proliferation assays
Cell Counting Kit-8 (CCK-8) was used to estimate the cell proliferation
rate according to the manufacturer’s instructions (Meilunbio, MA0218).
Briefly, Panc-1 and CFPAC-1 cells were plated in 96-well plates at a
density of 1–5 × 10^3 cells/well and the optical density at 450 nm
(OD450) was measured (Biotek, USA) every 24 or 48 h for 5 days. For
colony formation assays, 1 × 10^3 cells were seeded in six-well plates
for 10 days, and surviving colonies were stained and counted using 1%
crystal violet (Beyotime, C0121). EdU cell proliferation staining was
performed using an EdU kit (BeyoClick™ EdU Cell Proliferation Kit with
Alexa Fluor 555; Beyotime, [334]C10338) according to the manufacturer’s
instructions. Panc-1 (1 × 10^6) or CFPAC-1 cells (3 × 10^5) were seeded
in six-well plates overnight and were incubated with EdU for 2 h.
Transwell migration and invasion assays
Panc-1 (1–5 × 10^4) and CFPAC-1 (1 × 10^5) were seeded into Transwell
inserts (8-μm pore size, BD Falcon) for migration assays. For the
invasion assay, inserts were pre-coated with Matrigel (Corning,
354234), according to the manufacturer’s instructions. After 24 h of
culture for the migration assays or 48 h for the invasion assays, the
wells were stained with crystal violet (Beyotime, C0121). Migrated
cells number was quantified in an automated fashion using the ImageJ
v1.53e software.
Cell cycle and apoptosis assays
For the cell cycle assay, 1 × 10^6 cells were collected and fixed in
75% ethanol at 4 °C overnight. DNA was stained using propidium iodide
(PI)/RNase Staining Buffer (BD Biosciences, USA), according to the
manufacturer’s instructions. For the apoptotic assay, 1 × 10^6 cells
were incubated with 5 μl of FITC-conjugated annexin V (BD Biosciences,
556419) and 5 μl of PI (BD Biosciences, 550825) for 15 min at room
temperature in the dark. Flow cytometry was performed using FACS
(Becton-Dickinson, Bedford, MA, USA), and the results were analyzed
using the FlowJo V10 software.
Enzyme-linked immunosorbent assay (ELISA)
The levels of IL-1α and IL-6 in the CM samples from CAFs and Schwann
cells were quantified using a commercial ELISA kit (MultiSciences,
EK101A, EK301A, and EK106), according to the manufacturer’s
instructions.
Protein extraction and western blot assay
Total protein was extracted from cell pellets lysed with RIPA buffer
(Sigma-Aldrich, R0278) containing 5 mM EDTA, 1 × Halt phosphatase
inhibitor cocktail (Thermo Fisher Scientific, 78420), and 1 × Halt
protease inhibitor cocktail (Thermo Fisher Scientific, 78429). For
western blotting, an estimated 10–40 µg protein was loaded per well on
a 10% SDS-PAGE gel and transferred onto a PVDF membrane (Thermo Fisher
Scientific) and incubated with antibodies against α-SMA (Abcam, ab5694,
1:1000), FAP (CST, 66562, 1:1000), GAPDH (Santa Cruz Biotechnology,
sc-47724, 1:1000), E-cadherin (CST, 3195, 1:1000), N-cadherin
(CST,13116, 1:1000), Vimentin (CST, 5741, 1:2000), Fibronectin (CST,
26836, 1:2000), and β-catenin (CST, 8480, 1:1000) after blocking.
Recombined protein and protein neutralization
For recombined protein, 10 ng/ml recombined human MDK (PeproTech,
450-16) were incubated with Panc-1 cells with for 24–48 h. About
1 ng/ml recombined human IL-1α (R&D systems, 200-LA-002/CF) were
incubated with CAFs for 24 h for in vitro assays or 10 ng/ml for 72 h
for in vivo assay. For protein neutralization, CM of Schwann cells were
neutralized with 10 μg/mL anti-Midkine (Santa Cruz Biotechnology,
sc-46701) and IL-1α (Proteintech, 16765-1-AP) for 1 h at 37 °C. Normal
rabbit IgG (10 μg/mL, CST, 2729) alone was used as the control.
RNA extraction, cDNA synthesis, and real-time PCR (qPCR)
Total RNA was extracted using TRIzol Reagent (Life Technologies,
15596026), and reverse transcription was performed using HiScript®
Reverse Transcriptase (Vazyme, China, R101-02), according to the
manufacturer’s instructions. For qPCR analysis, double-stranded cDNA
was amplified using an SYBR Green PCR Kit (Vazyme, Q712) and detected
using qTOWER384G (Analytikjena). The primer sequences used in this
study are listed in Supplementary Table [335]S2.
Statistical analysis
Statistical analyses were performed using GraphPad Prism version 8.0.
IHC/IF positive area and intensity were calculated by QuPath Version
0.1.2 ([336]https://qupath.github.io/). Patients in the tissue
microarray were divided into low and high SC area/iCAFs number groups
according to the optimal cut-off value calculated using the X-Tile
software^[337]76. K–M curve and Cox regression analysis was performed
to assess the association with overall survival using SPSS v23 (IBM
Inc.). All statistical tests were indicated in the figure legends.
Reporting summary
Further information on research design is available in the [338]Nature
Portfolio Reporting Summary linked to this article.
Supplementary information
[339]Supplementary Information^ (14.3MB, docx)
[340]41467_2023_40314_MOESM2_ESM.pdf^ (30.8KB, pdf)
Description of Additional Supplementary Files
[341]Supplementary Data 1^ (1.5MB, xlsx)
[342]Supplementary Data 2^ (1.3MB, xlsx)
[343]Supplementary Data 3^ (243.6KB, xlsx)
[344]Reporting Summary^ (163.4KB, pdf)
Acknowledgements