Objective T cells play a key role in the pathogenesis of early systemic sclerosis. This study was undertaken to assess the safety and efficacy of abatacept in patients with diffuse cutaneous systemic sclerosis (dcSSc). Methods In this 12‐month, randomized, double‐blind, placebo‐controlled trial, participants were randomized 1:1 to receive either subcutaneous abatacept 125 mg or matching placebo, stratified by duration of dcSSc. Escape therapy was allowed at 6 months for worsening disease. The coprimary end points were change in the modified Rodnan skin thickness score (MRSS) compared to baseline and safety over 12 months. Differences in longitudinal outcomes were assessed according to treatment using linear mixed models, with outcomes censored after initiation of escape therapy. Skin tissue obtained from participants at baseline was classified into intrinsic gene expression subsets. Results Among 88 participants, the adjusted mean change in the MRSS at 12 months was −6.24 units for those receiving abatacept and −4.49 units for those receiving placebo, with an adjusted mean treatment difference of −1.75 units (P = 0.28). Outcomes for 2 secondary measures (Health Assessment Questionnaire disability index and a composite measure) were clinically and statistically significantly better with abatacept. The proportion of subjects in whom escape therapy was needed was higher in the placebo group relative to the abatacept group (36% versus 16%). In the inflammatory and normal‐like skin gene expression subsets, decline in the MRSS over 12 months was clinically and significantly greater in the abatacept group versus the placebo group (P < 0.001 and P = 0.03, respectively). In the abatacept group, adverse events occurred in 35 participants versus 40 participants in the placebo group, including 2 deaths and 1 death, respectively. Conclusion In this phase II trial, abatacept was well‐tolerated, but change in the MRSS was not statistically significant. Secondary outcome measures, including gene expression subsets, showed evidence in support of abatacept. These data should be confirmed in a phase III trial.

Spatial patterns of gene expression manifest at scales ranging from local (e.g., cell-cell interactions) to global (e.g., body axis patterning). However, current spatial transcriptomics methods either average local contexts or are restricted to limited fields of view. Here, we introduce sci-Space, which retains single-cell resolution while resolving spatial heterogeneity at larger scales. Applying sci-Space to developing mouse embryos, we captured approximate spatial coordinates and whole transcriptomes of about 120,000 nuclei. We identify thousands of genes exhibiting anatomically patterned expression, leverage spatial information to annotate cellular subtypes, show that cell types vary substantially in their extent of spatial patterning, and reveal correlations between pseudotime and the migratory patterns of differentiating neurons. Looking forward, we anticipate that sci-Space will facilitate the construction of spatially resolved single-cell atlases of mammalian development.

ObjectivesDetermine global skin transcriptome patterns of early diffuse systemic sclerosis (SSc) and how they differ from later disease.MethodsSkin biopsy RNA from 48 patients in the Prospective Registry for Early Systemic Sclerosis (PRESS) cohort (mean disease duration 1.3 years) and 33 matched healthy controls was examined by next-generation RNA sequencing. Data were analysed for cell type-specific signatures and compared with similarly obtained data from 55 previously biopsied patients in Genetics versus Environment in Scleroderma Outcomes Study cohort with longer disease duration (mean 7.4 years) and their matched controls. Correlations with histological features and clinical course were also evaluated.ResultsSSc patients in PRESS had a high prevalence of M2 (96%) and M1 (94%) macrophage and CD8 T cell (65%), CD4 T cell (60%) and B cell (69%) signatures. Immunohistochemical staining of immune cell markers correlated with the gene expression-based immune cell signatures. The prevalence of immune cell signatures in early diffuse SSc patients was higher than in patients with longer disease duration. In the multivariable model, adaptive immune cell signatures were significantly associated with shorter disease duration, while fibroblast and macrophage cell type signatures were associated with higher modified Rodnan Skin Score (mRSS). Immune cell signatures also correlated with skin thickness progression rate prior to biopsy, but did not predict subsequent mRSS progression.ConclusionsSkin in early diffuse SSc has prominent innate and adaptive immune cell signatures. As a prominently affected end organ, these signatures reflect the preceding rate of disease progression. These findings could have implications in understanding SSc pathogenesis and clinical trial design.

Supplementary data are available at Bioinformatics online.

Kisspeptin stimulates gonadotropin-releasing hormone (GnRH) neurons via the kisspeptin receptor, Kiss1r. In rodents, estrogen-responsive kisspeptin neurons in the rostral hypothalamus have been postulated to mediate estrogen-induced positive feedback induction of the preovulatory luteinizing hormone (LH) surge. However, conflicting evidence exists regarding the ability of mice lacking Kiss1r to display LH surges in response to exogenous hormones. Whether the discrepancy reflects different mouse strains used and/or utilization of different surge-induction paradigms is unknown. Here, we tested multiple hormonal paradigms in one Kiss1r knockout (KO) model to see which paradigms, if any, could generate circadian-timed LH surges. Kiss1r KO and wild-type (WT) females were ovariectomized, given sex steroids in various modes, and assessed several days later for LH levels in the morning or evening (when surges occur). Serum LH levels were very low in all morning animals, regardless of genotype or hormonal paradigm. In each paradigm, virtually all WT females displayed clear LH surges in the evening, whereas none of the KO females demonstrated LH surges. The lack of LH surges in KO mice reflects a lack of GnRH secretion rather than diminished pituitary responsiveness from a lifetime lack of GnRH exposure because KO mice responded to GnRH priming with robust LH secretion. Moreover, high cfos-GnRH coexpression was detected in WT females in the evening, whereas low cfos-GnRH coexpression was present in KO females at all time points. Our findings conclusively demonstrate that WT females consistently display LH surges under multiple hormonal paradigms, whereas Kiss1r KO mice do not, indicating that kisspeptin-Kiss1r signaling is mandatory for GnRH/LH surge induction.

Objective. High-throughput gene expression profiling of tissue samples from patients with systemic sclerosis (SSc) has identified 4 "intrinsic" gene expression subsets: inflammatory, fibroproliferative, normal-like, and limited. Prior methods required agglomerative clustering of many samples. In order to classify individual patients in clinical trials or for diagnostic purposes, supervised methods that can assign single samples to molecular subsets are required. We undertook this study to introduce a novel machine learning classifier as a robust accurate intrinsic subset predictor.Methods. Three independent gene expression cohorts were curated and merged to create a data set covering 297 skin biopsy samples from 102 unique patients and controls, which was used to train a machine learning algorithm. We performed external validation using 3 independent SSc cohorts, including a gene expression data set generated by an independent laboratory on a different microarray platform. In total, 413 skin biopsy samples from 213 individuals were analyzed in the training and testing cohorts.Results. Repeated cross-fold validation identified consistent and discriminative markers using multinomial elastic net, performing with an average classification accuracy of 87.1% with high sensitivity and specificity. In external validation, the classifier achieved an average accuracy of 85.4%. Reanalyzing data from a previous study, we identified subsets of patients that represent the canonical inflammatory, fibroproliferative, and normal-like subsets.Conclusion. We developed a highly accurate classifier for SSc molecular subsets for individual patient samples. The method can be used in SSc clinical trials to identify an intrinsic subset on individual samples. Our method provides a robust data-driven approach to aid clinical decision-making and interpretation of heterogeneous molecular information in SSc patients.

Fewer than half of patients with systemic sclerosis demonstrate modified Rodnan skin score improvement during mycophenolate mofetil (MMF) treatment. To understand the molecular basis for this observation, we extended our prior studies and characterized molecular and cellular changes in skin biopsies from subjects with systemic sclerosis treated with MMF. Eleven subjects completed ≥24 months of MMF therapy. Two distinct skin gene expression trajectories were observed across six of these subjects. Three of the six subjects showed attenuation of the inflammatory signature by 24 months, paralleling reductions in CCL2 mRNA expression in skin and reduced numbers of macrophages and myeloid dendritic cells in skin biopsies. MMF cessation at 24 months resulted in an increased inflammatory score, increased CCL2 mRNA and protein levels, modified Rodnan skin score rebound, and increased numbers of skin myeloid cells in these subjects. In contrast, three other subjects remained on MMF >24 months and showed a persistent decrease in inflammatory score, decreasing or stable modified Rodnan skin score, CCL2 mRNA reductions, sera CCL2 protein levels trending downward, reduction in monocyte migration, and no increase in skin myeloid cell numbers. These data summarize molecular changes during MMF therapy that suggest reduction of innate immune cell numbers, possibly by attenuating expression of chemokines, including CCL2.

ObjectiveWe sought to determine histologic and gene expression features of clinical improvement in early diffuse cutaneous systemic sclerosis (dcSSc; scleroderma).MethodsFifty-eight forearm biopsies were evaluated from 26 individuals with dcSSc in two clinical trials. Histologic/immunophenotypic assessments of global severity, alpha-smooth muscle actin (aSMA), CD34, collagen, inflammatory infiltrate, follicles and thickness were compared with gene expression and clinical data. Support vector machine learning was performed using scleroderma gene expression subset (normal-like, fibroproliferative, inflammatory) as classifiers and histology scores as inputs. Comparison of w-vector mean absolute weights was used to identify histologic features most predictive of gene expression subset. We then tested for differential gene expression according to histologic severity and compared those with clinical improvement (according to the Combined Response Index in Systemic Sclerosis).ResultsaSMA was highest and CD34 lowest in samples with highest local Modified Rodnan Skin Score. CD34 and aSMA changed significantly from baseline to 52 weeks in clinical improvers. CD34 and aSMA were the strongest predictors of gene expression subset, with highest CD34 staining in the normal-like subset (p<0.001) and highest aSMA staining in the inflammatory subset (p=0.016). Analysis of gene expression according to CD34 and aSMA binarised scores identified a 47-gene fibroblast polarisation signature that decreases over time only in improvers (vs non-improvers). Pathway analysis of these genes identified gene expression signatures of inflammatory fibroblasts.ConclusionCD34 and aSMA stains describe distinct fibroblast polarisation states, are associated with gene expression subsets and clinical assessments, and may be useful biomarkers of clinical severity and improvement in dcSSc.