A single-cell atlas of IL-23 inhibition in cutaneous psoriasis distinguishes clinical response.
David WuAshley A HailerSijia WangMichelle YuanJamie ChanAbdallah KurdiMaha RahimAyano KondoDavid HanHira AliH Blaize D'AngioAaron T MayerDaniel KlufasEsther KimA Hunter ShainJaehyuk ChoiTina BhutaniGregory SimpsonRoy C GrekinRoberto R Ricardo-GonzalezElizabeth PurdomJeffrey P NorthJeffrey B ChengRaymond J ChoPublished in: Science immunology (2024)
Psoriasis vulgaris and other chronic inflammatory diseases improve markedly with therapeutic blockade of interleukin-23 (IL-23) signaling, but the genetic mechanisms underlying clinical responses remain poorly understood. Using single-cell transcriptomics, we profiled immune cells isolated from lesional psoriatic skin before and during IL-23 blockade. In clinically responsive patients, a psoriatic transcriptional signature in skin-resident memory T cells was strongly attenuated. In contrast, poorly responsive patients were distinguished by persistent activation of IL-17-producing T (T17) cells, a mechanism distinct from alternative cytokine signaling or resistance isolated to epidermal keratinocytes. Even in IL-23 blockade-responsive patients, we detected a recurring set of recalcitrant, disease-specific transcriptional abnormalities. This irreversible immunological state may necessitate ongoing IL-23 inhibition. Spatial transcriptomic analyses also suggested that successful IL-23 blockade requires dampening of >90% of IL-17-induced response in lymphocyte-adjacent keratinocytes, an unexpectedly high threshold. Collectively, our data establish a patient-level paradigm for dissecting responses to immunomodulatory treatments.
Keyphrases
- single cell
- end stage renal disease
- ejection fraction
- newly diagnosed
- chronic kidney disease
- rheumatoid arthritis
- rna seq
- gene expression
- prognostic factors
- magnetic resonance imaging
- wound healing
- transcription factor
- magnetic resonance
- peritoneal dialysis
- computed tomography
- cancer therapy
- dna methylation
- ankylosing spondylitis
- disease activity
- patient safety
- big data
- genome wide
- artificial intelligence
- data analysis