Back

High-resolution spatial profiling identifies disease-specific molecular architecture in palmoplantar pustulosis

Yatsuzuka, K.; Muto, J.; Mizukami, Y.; Isayama, K.; Shiokawa, D.; Miyazaki, M.; Tsuda, T.; Shiraishi, K.; Fujisawa, Y.; Murakami, M.

2026-05-12 molecular biology
10.64898/2026.05.08.723901 bioRxiv
Show abstract

Palmoplantar pustulosis (PPP) and dyshidrotic eczema (DE) are chronic vesiculopustular dermatoses with overlapping clinical presentations but distinct underlying biology. Although comparative transcriptomic and proteomic analyses between PPP and DE have been reported, they remain limited in number and scope, with no comprehensive understanding of their distinct molecular signatures. Moreover, their molecular mechanisms remain unclear, and currently available therapeutic options are limited. To clarify disease-specific epidermal programs underlying vesicle formation, we conducted Visium HD spatial transcriptomic analysis of FFPE lesional skin samples obtained from patients with PPP and DE, followed by immunohistochemical validation against normal palmoplantar skin controls. Spatial clustering identified a keratinocyte subpopulation adjacent to vesicles that exhibited distinct transcriptional programs in the two diseases. In PPP, vesicle-associated keratinocytes demonstrated marked downregulation of aquaporin-3 (AQP3) and E-cadherin, together with strong, spatially localized activation of JAK-STAT3 signaling. Conversely, DE exhibited diffuse AQP3 expression and more homogeneous activation of JAK-STAT3 signaling throughout the epidermis. These results indicate that, although PPP and DE share inflammatory pathways, they differ substantially in their spatial molecular architecture. Reduced AQP3 expression and localized STAT3 activation may contribute to vesicle formation in PPP, supporting our previous hypothesis that implicates intraepidermal sweat leakage as a pathogenic mechanism in PPP. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=130 SRC="FIGDIR/small/723901v1_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@19c7591org.highwire.dtl.DTLVardef@eab29aorg.highwire.dtl.DTLVardef@73c2e2org.highwire.dtl.DTLVardef@1ffc02f_HPS_FORMAT_FIGEXP M_FIG C_FIG

Matching journals

The top 1 journal accounts for 50% of the predicted probability mass.

1
Journal of Investigative Dermatology
42 papers in training set
Top 0.1%
53.7%
50% of probability mass above
2
Journal of Advanced Research
15 papers in training set
Top 0.1%
4.1%
3
Nature Communications
4913 papers in training set
Top 42%
3.2%
4
BMC Medical Genomics
36 papers in training set
Top 0.2%
2.4%
5
Arthritis & Rheumatology
33 papers in training set
Top 0.2%
2.2%
6
Advanced Science
249 papers in training set
Top 8%
2.2%
7
JCI Insight
241 papers in training set
Top 3%
2.0%
8
PLOS ONE
4510 papers in training set
Top 49%
2.0%
9
Scientific Reports
3102 papers in training set
Top 56%
1.7%
10
eLife
5422 papers in training set
Top 45%
1.5%
11
Disease Models & Mechanisms
119 papers in training set
Top 2%
1.3%
12
eBioMedicine
130 papers in training set
Top 3%
1.0%
13
Experimental Dermatology
10 papers in training set
Top 0.2%
1.0%
14
Human Mutation
29 papers in training set
Top 0.6%
0.9%
15
Computational and Structural Biotechnology Journal
216 papers in training set
Top 9%
0.8%
16
International Journal of Molecular Sciences
453 papers in training set
Top 14%
0.8%
17
Frontiers in Immunology
586 papers in training set
Top 8%
0.7%
18
Cell Reports Medicine
140 papers in training set
Top 9%
0.7%
19
Journal of Translational Medicine
46 papers in training set
Top 3%
0.7%
20
Human Genomics
21 papers in training set
Top 0.4%
0.7%
21
Journal of Allergy and Clinical Immunology
25 papers in training set
Top 0.9%
0.7%
22
Matrix Biology
28 papers in training set
Top 0.5%
0.5%
23
Frontiers in Medicine
113 papers in training set
Top 8%
0.5%
24
Blood
67 papers in training set
Top 2%
0.5%
25
EBioMedicine
39 papers in training set
Top 2%
0.5%
26
Clinical and Translational Medicine
30 papers in training set
Top 2%
0.5%