scRNA-sequencing data and metadata for psoriatic arthritis (PsA) patients that were responders or non-responders to anti-TNF treatment as well as healthy controls

<b>scDrugPrio: A framework for</b><b> </b><b>the</b><b> </b><b>analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases</b>Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs. In our recent work, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs.scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive <i>in vitro, in vivo,</i><i> </i>and <i>in silico</i><i> </i>studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn’s disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn’s disease patients. The analysis showed great variations in drug predictions between patients, for example,assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment. Application to individual patients indicates scDrugPrio’s potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package (https://github.com/SDTC-CPMed/scDrugPrio).<br><br>PsA patients were recruited from different rheumatology departments from university hospitals belonging to the IMIDC. All PsA patients were diagnosed according to the CASPAR diagnostic criteria for PsA (34) with &gt; 1 year of disease evolution and &gt; 18 years old at the time of recruitment. Exclusion criteria for PsA included the presence of any other form of inflammatory arthritis, rheumatoid factor levels greater than twice the normality threshold or confirmed presence of an inflammatory bowel disease. PBMCs were sampled prior to treatment with anti-TNF and cryopreserved. Treatment response was classified at week 12 according to the EULAR response<b>. </b>For the anti-TNF study, 6 males and 10 females were included. Simultaneously, healthy age- and sex-matched control subjects were recruited from healthy volunteers recruited through the Vall d’Hebron University Hospital in Barcelona (Spain). All the controls were screened for the presence of any autoimmune disorder, as well as for first-degree family occurrence of autoimmune diseases. None were found to be positive. All in all, four males and four females were included as controls.Patients in the study of PBMC from patients with psoriatic arthritis consented to participate in this study as approved by Hospital Universitari Vall d'Hebron Clinical Research Ethics Committee with reference number 20/0022. Protocols were reviewed and approved by the local institutional review board of each participating centre. This research conformed to the principles of the Helsinki Declaration.In summary, this data set includes raw scRNA-seq data and metadata of pre-treatment PBMC of psoriatic arthritis patients that did or did not respond to anti-TNF treatment as well as untreated healthy controls. The RDS file also includes the deep count auto encoder (DCA) denoised scRNA-seq matrix as well as clustering outcomes.

scDrugPrio：用于单细胞转录组学分析的框架，以解决免疫介导性炎症疾病（immune-mediated inflammatory diseases, IMIDs）精准医学中的多重难题

免疫介导性炎症疾病（IMIDs）患者群体中，药物治疗无效是普遍存在的临床难题，其核心诱因之一在于，针对IMIDs复杂且异质性的细胞与分子变化特征，目前仍缺乏用于药物优先级排序与重定位的系统性解决方案。在本团队近期研究中，我们提出了一款计算框架scDrugPrio，其可基于单细胞RNA测序（single-cell RNA sequencing, scRNA-seq）数据构建炎症疾病网络模型。scDrugPrio可构建精细化的炎症疾病网络模型，整合细胞类型特异性表达变化、细胞间通讯改变与药物药理学特性等多维度信息，实现对数千种药物的筛选与优先级排序。

scDrugPrio基于抗原诱导性关节炎小鼠模型开发，并通过获批药物的精准率-召回率提升效果得到验证，同时针对研究疾病中预测有效但尚未获批的药物开展了大量体外（in vitro）、体内（in vivo）与虚拟（in silico）实验，进一步验证了框架的有效性。后续，研究团队将scDrugPrio应用于多发性硬化、克罗恩病与银屑病关节炎（psoriatic arthritis, PsA）场景，通过对相关获批药物的优先级排序，进一步验证了框架的适用性。

但与关节炎小鼠模型不同，同一确诊病症的患者间存在显著的细胞与基因表达异质性，这类差异或可解释部分患者对治疗应答与否的临床现象。通过将scDrugPrio应用于11名克罗恩病患者的scRNA-seq数据，该假说得到了验证。分析结果显示，不同患者的药物预测结果存在显著差异：例如，在对TNF拮抗剂治疗有应答的患者中，该药物被赋予较高优先级，而在无应答患者中则被评为低优先级。

将scDrugPrio应用于个体患者的分析结果表明，其具备在细胞组、基因组与药物组全尺度下开展个性化网络药物筛选的潜力。为此，我们将scDrugPrio封装为一款易用的R包（https://github.com/SDTC-CPMed/scDrugPrio）。

本研究的银屑病关节炎（PsA）患者招募自IMIDC下属大学医院的不同风湿科门诊。所有PsA患者均符合CASPAR银屑病关节炎诊断标准，且病程超过1年、入组时年龄≥18岁。PsA患者的排除标准包括：合并其他类型炎症性关节炎、类风湿因子水平超过正常阈值两倍，或确诊存在炎症性肠病。研究人员在患者接受TNF拮抗剂治疗前采集外周血单个核细胞（peripheral blood mononuclear cells, PBMCs）并进行冻存留存。治疗应答情况于给药第12周依据EULAR应答标准进行分级。本次TNF拮抗剂研究共纳入6名男性与10名女性患者。

同时，研究团队从西班牙巴塞罗那瓦尔德希伯伦大学医院招募的健康志愿者中，筛选出年龄与性别匹配的健康对照人群。所有对照个体均接受自身免疫性疾病及一级亲属自身免疫病史筛查，结果均为阴性。最终共纳入4名男性与4名女性作为健康对照。

本银屑病关节炎PBMC研究的所有患者均签署了知情同意书，该研究经瓦尔德希伯伦大学医院临床研究伦理委员会批准（批准号：20/0022），各参与中心的伦理委员会均对研究方案进行了审核与批准。本研究符合《赫尔辛基宣言》的相关原则。

综上，本数据集包含接受TNF拮抗剂治疗前的银屑病关节炎患者（含应答者与无应答者）以及未接受治疗的健康对照的原始scRNA-seq数据与元数据。该RDS文件同时包含经深度计数自编码器（deep count auto encoder, DCA）降噪后的scRNA-seq表达矩阵与聚类分析结果。