The respiratory system, including the lungs, is essential for terrestrial life. While recent research has advanced our understanding of lung development, much still relies on animal models and transcriptome analyses. In this study conducted within the Human Developmental Cell Atlas (HDCA) initiative, we describe the protein-level spatiotemporal organization of the lung during the first trimester of human gestation. Using high-parametric tissue imaging with a 30-plex antibody panel, we analyzed human lung samples from 6 to 13 post-conception weeks, generating data from over 2 million cells across five developmental timepoints. We present a resource detailing spatially resolved cell type composition of the developing human lung, including proliferative states, immune cell patterns, spatial arrangement traits, and their temporal evolution. This represents an extensive single-cell resolved protein-level examination of the developing human lung and provides a valuable resource for further research into the developmental roots of human respiratory health and disease. Understanding the development of the lung will inform treatments for congenital diseases and approaches for preterm infant care. Here they map human lung development using high-parametric imaging at the single-cell level to track abundance, proliferation, and spatial organization of key cell types during early gestation.

The lung contains numerous specialized cell types with distinct roles in tissue function and integrity. To clarify the origins and mechanisms generating cell heterogeneity, we created a comprehensive topographic atlas of early human lung development. Here we report 83 cell states and several spatially resolved developmental trajectories and predict cell interactions within defined tissue niches. We integrated single-cell RNA sequencing and spatially resolved transcriptomics into a web-based, open platform for interactive exploration. We show distinct gene expression programmes, accompanying sequential events of cell differentiation and maturation of the secretory and neuroendocrine cell types in proximal epithelium. We define the origin of airway fibroblasts associated with airway smooth muscle in bronchovascular bundles and describe a trajectory of Schwann cell progenitors to intrinsic parasympathetic neurons controlling bronchoconstriction. Our atlas provides a rich resource for further research and a reference for defining deviations from homeostatic and repair mechanisms leading to pulmonary diseases.

Objectives Results from the SCOT (Scleroderma: Cyclophosphamide Or Transplantation) clinical trial demonstrated significant benefits of haematopoietic stem cell transplant (HSCT) versus cyclophosphamide (CTX) in patients with systemic sclerosis. The objective of this study was to test the hypothesis that transplantation stabilises the autoantibody repertoire in patients with favourable clinical outcomes. Methods We used a bead-based array containing 221 protein antigens to profile serum IgG autoantibodies in participants of the SCOT trial. Results Comparison of autoantibody profiles at month 26 (n=23 HSCT; n=22 CTX) revealed antibodies against two viral antigens and six self-proteins (SSB/La, CX3CL1, glycyl-tRNA synthetase (EJ), parietal cell antigen, bactericidal permeability-increasing protein and epidermal growth factor receptor (EGFR)) that were significantly different between treatment groups. Linear mixed model analysis identified temporal increases in antibody levels for hepatitis B surface antigen, CCL3 and EGFR in HSCT-treated patients. Eight of 32 HSCT-treated participants and one of 31 CTX-treated participants had temporally varying serum antibody profiles for one or more of 14 antigens. Baseline autoantibody levels against 20 unique antigens, including 9 secreted proteins (interleukins, IL-18, IL-22, IL-23 and IL-27), interferon-α2A, stem cell factor, transforming growth factor-β, macrophage colony-stimulating factor and macrophage migration inhibitory factor were significantly higher in patients who survived event-free to month 54. Conclusions Our results suggest that HSCT favourably alters the autoantibody repertoire, which remains virtually unchanged in CTX-treated patients. Although antibodies recognising secreted proteins are generally thought to be pathogenic, our results suggest a subset could potentially modulate HSCT in scleroderma.

The lung contains numerous specialized cell-types with distinct roles in tissue function and integrity. To clarify the origins and mechanisms generating cell heterogeneity, we created a first comprehensive topographic atlas of early human lung development. We report 83 cell states, several spatially-resolved developmental trajectories and predict cell interactions within defined tissue niches. We integrated scRNA-Seq and spatial transcriptomics into a web-based, open platform for interactive exploration. To illustrate the utility of our approach we show distinct states of secretory and neuroendocrine cells, largely overlapping with the programs activated either during lung fibrosis or small cell lung cancer progression. We define the origin of uncharacterized airway fibroblasts associated with airway smooth muscle in bronchovascular bundles, and describe a trajectory of Schwann cell progenitors to intrinsic parasympathetic neurons controlling bronchoconstriction. Our atlas provides a rich resource for further research and a reference for defining deviations from homeostatic and repair mechanisms leading to pulmonary diseases.

ABSTRACT Cellular division is a fundamental source of cell-to-cell variability, and studies of transcript and protein abundances have revealed several hundred genes that are regulated by the cell cycle 1–8 . However, none of these studies provide single-cell resolution of protein expression, leaving an incomplete understanding of cell-to-cell heterogeneity and the roles of cycling transcripts and proteins. Here, we present the first comprehensive map of spatiotemporal heterogeneity of the human proteome by integrating proteomics at subcellular resolution, single-cell transcriptomics, and pseudotime measurements of individual cells within the cell cycle. We identify that 17% of the human proteome displays cell-to-cell variability, of which 26% is correlated to cell cycle progression, and we present the first evidence of cell cycle association for 235 proteins. Only 15% of proteomic cell cycle regulation is due to transcriptomic cycling, which points to other means of regulation such as post-translational modifications. For proteins regulated at the transcript level, we observe a 7.7 hour delay between peak expression of transcript and protein on average. This spatially resolved proteomic map of the cell cycle has been integrated into the Human Protein Atlas and serves as a valuable resource for accelerating molecular studies of the human cell cycle and cell proliferation.

Background: Duchenne Muscular Dystrophy is a severe, incurable disorder caused by mutations in the dystrophin gene. The disease is characterized by decreased muscle function, impaired muscle regeneration and increased inflammation. In a clinical context, muscle deterioration, is evaluated using physical tests and analysis of muscle biopsies, which fail to accurately monitor the disease progression. Objectives: This study aims to confirm and asses the value of blood protein biomarkers as disease progression markers using one of the largest longitudinal collection of samples. Methods: A total of 560 samples, both serum and plasma, collected at three clinical sites are analyzed using a suspension bead array platform to assess 118 proteins targeted by 250 antibodies in microliter amount of samples. Results: Nine proteins are confirmed as disease progression biomarkers in both plasma and serum. Abundance of these biomarkers decreases as the disease progresses but follows different trajectories. While carbonic anhydrase 3, microtubule associated protein 4 and collagen type I alpha 1 chain decline rather constantly over time, myosin light chain 3, electron transfer flavoprotein A, troponin T, malate dehydrogenase 2, lactate dehydrogenase B and nestin plateaus in early teens. Electron transfer flavoprotein A, correlates with the outcome of 6-minutes-walking-test whereas malate dehydrogenase 2 together with myosin light chain 3, carbonic anhydrase 3 and nestin correlate with respiratory capacity. Conclusions: Nine biomarkers have been identified that correlate with disease milestones, functional tests and respiratory capacity. Together these biomarkers recapitulate different stages of the disorder that, if validated can improve disease progression monitoring.

We expand Mendelian Randomization (MR) methodology to deal with randomly missing data on either the exposure or the outcome variable, and furthermore with data from nonindependent individuals (eg components of a family). Our method rests on the Bayesian MR framework proposed by Berzuini et al (2018), which we apply in a study of multiplex Multiple Sclerosis (MS) Sardinian families to characterise the role of certain plasma proteins in MS causation. The method is robust to presence of pleiotropic effects in an unknown number of instruments, and is able to incorporate inter-individual kinship information. Introduction of missing data allows us to overcome the bias introduced by the (reverse) effect of treatment (in MS cases) on level of protein. From a substantive point of view, our study results confirm recent suspicion that an increase in circulating IL12A and STAT4 protein levels does not cause an increase in MS risk, as originally believed, suggesting that these two proteins may not be suitable drug targets for MS.

Multiple sclerosis (MS) is a chronic inflammatory, likely autoimmune disease of the central nervous system with a combination of genetic and environmental risk factors, among which Epstein-Barr virus (EBV) infection is a strong suspect. We have previously identified increased autoantibody levels toward the chloride-channel protein Anoctamin 2 (ANO2) in MS. Here, IgG antibody reactivity toward ANO2 and EBV nuclear antigen 1 (EBNA1) was measured using bead-based multiplex serology in plasma samples from 8,746 MS cases and 7,228 controls. We detected increased anti-ANO2 antibody levels in MS (P = 3.5 × 10-36) with 14.6% of cases and 7.8% of controls being ANO2 seropositive (odds ratio [OR] = 1.6; 95% confidence intervals [95%CI]: 1.5 to 1.8). The MS risk increase in ANO2-seropositive individuals was dramatic when also exposed to 3 known risk factors for MS: HLA-DRB1*15:01 carriage, absence of HLA-A*02:01, and high anti-EBNA1 antibody levels (OR = 24.9; 95%CI: 17.9 to 34.8). Reciprocal blocking experiments with ANO2 and EBNA1 peptides demonstrated antibody cross-reactivity, mapping to ANO2 [aa 140 to 149] and EBNA1 [aa 431 to 440]. HLA gene region was associated with anti-ANO2 antibody levels and HLA-DRB1*04:01 haplotype was negatively associated with ANO2 seropositivity (OR = 0.6; 95%CI: 0.5 to 0.7). Anti-ANO2 antibody levels were not increased in patients from 3 other inflammatory disease cohorts. The HLA influence and the fact that specific IgG production usually needs T cell help provides indirect evidence for a T cell ANO2 autoreactivity in MS. We propose a hypothesis where immune reactivity toward EBNA1 through molecular mimicry with ANO2 contributes to the etiopathogenesis of MS.

Background In a study performed on multiplex Multiple Sclerosis (MS) Sardinian families to identify disease causing plasma proteins, application of Mendelian Randomization (MR) methods encounters difficulties due to relatedness of individuals, correlation between finely mapped genotype instrumental variables (IVs) and presence of missing exposures. Method We specialize the method of Berzuini et al (2018) to deal with these difficulties. The proposed method allows pedigree structure to enter the specification of the outcome distribution via kinship matrix, and treating missing exposures as additional parameters to be estimated from the data. It also acknowledges possible correlation between instruments by replacing the originally proposed independence prior for IV-specific pleiotropic effect with a g-prior. Based on correlated (r2< 0.2) IVs, we analysed the data of four candidate MS-causing proteins by using both the independence and the g-prior. Results 95 within the strictly negative real semiaxis, in both analyses, suggesting potential causality. Those instruments whose estimated pleiotropic effect exceeded 85 via frequentist MR gave inconsistent results. Replacing the independence with a g-prior led to smaller credible intervals for causal effect. Conclusions Bayesian MR may be a good way to study disease causation at a protein level based on family data and moderately correlated instruments.

Autoimmune regulator (AIRE) mutations result in autoimmune polyendocrinopathy-candidiasis-ectodermal dystrophy (APECED) syndrome characterized by defective central T cell tolerance and the production of many autoantibodies targeting tissue-specific antigens and cytokines. By studying CD3- and AIRE-deficient patients, we found that lack of either T cells or AIRE function resulted in the peripheral accumulation of autoreactive mature naïve B cells. Proteomic arrays and Biacore affinity measurements revealed that unmutated antibodies expressed by these autoreactive naïve B cells recognized soluble molecules and cytokines including insulin, IL-17A, and IL-17F, which are AIRE-dependent thymic peripheral tissue antigens targeted by autoimmune responses in APECED. AIRE-deficient patients also displayed decreased frequencies of regulatory T cells (Tregs) that lacked common TCRβ clones found instead in their conventional T cell compartment, thereby suggesting holes in the Treg TCR repertoire of these patients. Hence, AIRE-mediated T cell/Treg selection normally prevents the expansion of autoreactive naïve B cells recognizing peripheral self-antigens.

BACKGROUND:Duchenne muscular dystrophy (DMD) is a fatal disease for which no cure is available. Clinical trials have shown to be largely underpowered due to inter-individual variability and noisy outcome measures. The availability of biomarkers able to anticipate clinical benefit is highly needed to improve clinical trial design and facilitate drug development.METHODS:In this study, we aimed to appraise the value of protein biomarkers to predict prognosis and monitor disease progression or treatment outcome in patients affected by DMD. We collected clinical data and 303 blood samples from 157 DMD patients in three clinical centres; 78 patients contributed multiple blood samples over time, with a median follow-up time of 2 years. We employed linear mixed models to identify biomarkers that are associated with disease progression, wheelchair dependency, and treatment with corticosteroids and performed survival analysis to find biomarkers whose levels are associated with time to loss of ambulation.RESULTS:Our analysis led to the identification of 21 proteins whose levels significantly decrease with age and nine proteins whose levels significantly increase. Seven of these proteins are also differentially expressed in non-ambulant patients, and three proteins are differentially expressed in patients treated with glucocorticosteroids. Treatment with corticosteroids was found to partly counteract the effect of disease progression on two biomarkers, namely, malate dehydrogenase 2 (MDH2, P = 0.0003) and ankyrin repeat domain 2 (P = 0.0005); however, patients treated with corticosteroids experienced a further reduction on collagen 1 serum levels (P = 0.0003), especially following administration of deflazacort. A time to event analysis allowed to further support the use of MDH2 as a prognostic biomarker as it was associated with an increased risk of wheelchair dependence (P = 0.0003). The obtained data support the prospective evaluation of the identified biomarkers in natural history and clinical trials as exploratory biomarkers.CONCLUSIONS:We identified a number of serum biomarkers associated with disease progression, loss of ambulation, and treatment with corticosteroids. The identified biomarkers are promising candidate prognostic and surrogate biomarkers, which may support drug developers if confirmed in prospective studies. The serum levels of MDH2 are of particular interest, as they correlate with disease stage and response to treatment with corticosteroids, and are also associated with the risk of wheelchair dependency and pulmonary function.

With the increasing availability of collections of antibodies, their evaluation in terms of binding selectivity becomes an important but challenging task. Planar antigen microarrays are very suitable tools to address this task and provide a powerful proteomics platform for the characterization of the binding selectivity of antibodies toward thousands of antigens in parallel. In this chapter, we describe our in-house developed procedures for the generation of high-density planar antigen microarrays with over 21,000 features. We also provide the details of the assay protocol, which we routinely use for the assessment of binding selectivity of the polyclonal antibodies generated within the Human Protein Atlas.

With the increasing number of binding reagents for affinity-based investigations of the human proteome, high-throughput tools for the characterization of the used reagents become essential. For the analysis of binding selectivity, bead-based antigen arrays offer a miniaturized and parallelized assay platform to meet such needs, as they enable two-dimensional multiplexing to analyze up to 384 samples against up to 500 analytes in a single round of analysis. In this chapter, we describe our protocols for the generation of multiplex bead arrays built on immobilized protein fragments, as well as biotinylated peptides. Combined together, these two versions of antigen arrays offer a versatile approach for multiplexed characterization of antibody binding selectivity, off-target interactions, as well as mapping for the amino acids of epitopes involved in antibody binding.

The intimate interaction between the pathophysiology of the human host and the biology of the Plasmodium falciparum parasite results in a wide spectrum of disease outcomes in malaria. Development of severe disease is associated with a progressively augmented imbalance in pro- and anti-inflammatory responses to high parasite loads and sequestration of parasitized erythrocytes. Although these phenomena collectively constitute common denominators for the wide variety of discrete severe malaria manifestations, the mechanistic rationales behind discrepancies in outcome are poorly understood. Exploration of the human pathophysiological response by variations in protein profiles in plasma presents an excellent opportunity to increase the understanding. This is ultimately required for better prediction, prevention and treatment of malaria, which is essential for ongoing elimination and eradication efforts.

Objective Systemic Lupus Erythematosus (SLE) is a heterogeneous systemic autoimmune disease that is currently lacking specific diagnostic biomarkers. The diversity within the patients might obstruct clinical trials and could reflect differences in underlying pathogenesis. Our objective was to identify protein profiles that could be used for diagnosis and to identify molecular subgroups within SLE for patient stratification subjected to different treatment. Method In a cross-sectional study we performed protein profiling of 695 plasma samples from SLE patients and matched controls. This was achieved by utilising an antibody suspension bead array targeting 367 proteins. T-test and ROC analysis was performed to identify differences in the protein profiles between SLE and controls. Unsupervised K-means clustering was performed to identify data-driven SLE subgroups. Results We report that the novel proteins MMP1, SELE, and S100A12 can distinguish between SLE patients and controls with an area under curve of 0.80 in a ROC analysis. In addition, 28 proteins were found to show differences (corrected p-value<0.05) between SLE patients and controls. By unsupervised clustering we identified an IRF5, NOS3 and CLDN8-driven subgroup, an ARID2 and SELE-driven subgroup and one subgroup characterised by low SLC22A2 levels. Conclusion We have identified potential biomarkers of SLE that may be used to improve the diagnosis of SLE patients. Our suggested panel of biomarkers needs to be validated in an additional SLE cohort and also in relation to other systemic autoimmune diseases before it can be used as a diagnostic test. We have also identified subgroups characterised by different molecular patterns, indicating underlying pathogenic differences. These patient groups might benefit from different treatment strategies. Our work adds new information to today’s view of classifying the heterogeneous subgroups within SLE and the importance of personalised medicine.

The underlying molecular mechanisms of autoimmune diseases are poorly understood. To unravel the autoimmune processes across diseases, comprehensive and unbiased analyses of proteins targets recognized by the adaptive immune system are needed. Here we present an approach starting from high-density peptide arrays to characterize autoantibody repertoires and to identify new autoantigens. A set of ten plasma and serum samples from subjects with multiple sclerosis, narcolepsy, and without any disease diagnosis were profiled on a peptide array representing the whole proteome, hosting 2.2 million 12-mer peptides with a six amino acid lateral shift. On the basis of the IgG reactivities found on these whole-proteome peptide microarrays, a set of 23 samples was then studied on a targeted array with 174 000 12-mer peptides of single amino acid lateral shift. Finally, verification of IgG reactivities was conducted with a larger sample set (n = 448) using the bead-based peptide microarrays. The presented workflow employed three different peptide microarray formats to discover and resolve the epitopes of human autoantibodies and revealed two potentially new autoantigens: MAP3K7 in multiple sclerosis and NRXN1 in narcolepsy. The presented strategy provides insights into antibody repertoire reactivity at a peptide level and may accelerate the discovery and validation of autoantigens in human diseases.

Today, it is known that autoimmune diseases start a long time before clinical symptoms appear. Anti-citrullinated protein antibodies (ACPAs) appear many years before the clinical onset of rheumatoid arthritis (RA). However, it is still unclear if and how ACPAs are arthritogenic. To better understand the molecular basis of pathogenicity of ACPAs, we investigated autoantibodies reactive against the C1 epitope of collagen type II (CII) and its citrullinated variants. We found that these antibodies are commonly occurring in RA. A mAb (ACC1) against citrullinated C1 was found to cross-react with several noncitrullinated epitopes on native CII, causing proteoglycan depletion of cartilage and severe arthritis in mice. Structural studies by X-ray crystallography showed that such recognition is governed by a shared structural motif "RG-TG" within all the epitopes, including electrostatic potential-controlled citrulline specificity. Overall, we have demonstrated a molecular mechanism that explains how ACPAs trigger arthritis.