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B and T cells are integral parts of the immune system and are implicated in many diseases, e.g. autoimmunity. Towards understanding the biology of B and T cells and subsets thereof, their transcriptomes can be analyzed using single-cell RNA sequencing. In some studies, the V(D)J transcripts encoding the variable regions of the B- and T-cell antigen receptors have been removed before the analyses. However, a systematic analysis of the effects of including versus excluding these genes is currently lacking. We have investigated the effects of these transcripts on unsupervised clustering and down-stream analyses of single-cell RNA sequencing data from B and T cells. We found that exclusion of the B-/T-cell receptor genes prior to unsupervised clustering resulted in clusters that represented biologically meaningful subsets, such as subsets of memory B and memory T cells. Furthermore, pseudo-time and trajectory inference analyses of early B-lineage cells resulted in a developmental pathway from progenitor to immature B cells. In contrast, when the B-/T-cell receptor genes were not removed, with the PCs used for clustering consisting of up to 70% V-genes, this resulted in some clusters being defined exclusively by V-gene segments. These did not represent biologically meaningful subsets; for instance in the early B-lineage cells, these clusters contained cells representing all developmental stages. Thus, in studies of B and T cells, to derive biologically meaningful results, it is imperative to remove the gene sequences that encode B- and T-cell receptors. CC BY-NC 4.0Corresponding author: Inga-Lill Mårtensson, Department of Rheumatology and Inflammation Research, Institute of Medicine, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. Tel.:+46(0)703640068; E-mail: lill.martensson@rheuma.gu.seThis work was supported by the Swedish Research Council, grants 2018-03128 and 2021-01150 (ILM) and 2016-01576 (IG); the Swedish Cancer Foundation, grant 19 0464 (ILM); the Swedish Childhood Cancer Fund, grants PR2018-0170 and PR2020-0147 (ILM), and TJ2019-0098 (AC); Assar Gabrielsson’s Foundation, FB21-104 (AC); Patient Association for Rheumatic Diseases, R-94129 (ILM) and R-940945 (IG); ALF (agreement between the Government of Sweden and the County Councils), ALFGBG-719631 (IG); Adlerbertska stiftelsen (TS); and the IngaBritt och Arne Lundbergs Foundation (ILM, IG)

Extra-intestinal pathogenic Escherichia coli (ExPEC) strains are responsible for a large number of human infections globally. The management of infections caused by ExPEC has been complicated by the emergence of antimicrobial resistance, most importantly the increasing recognition of isolates producing extended-spectrum β-lactamases (ESBL). Herein, we used whole-genome sequencing (WGS) on ExPEC isolates for a comprehensive genotypic characterization. Twenty-one ExPEC isolates, nine with and 12 without ESBL-production, from 16 patients with suspected sepsis were sequenced on an Illumina MiSeq platform. Analysis of WGS data was performed with widely used bioinformatics software and tools for genotypic characterization of the isolates. A higher number of plasmids, virulence and resistance genes were observed in the ESBL-producing isolates than the non-ESBL-producing, although not statistically significant due to the low sample size. All nine ESBL-producing ExPEC isolates presented with at least one bla gene, as did three of the 12 without ESBL-production. Multi-locus sequence typing analysis revealed a diversity of sequence types whereas phylogroup A prevailed among isolates both with and without ESBL-production. In conclusion, this limited study shows that analysis of WGS data can be used for genotypic characterization of ExPEC isolates to obtain in-depth information of clinical relevance. CC BY 4.0Available online 17 October 2022Corresponding author: Diana TilevikThis research received no external financial support.

Objective Cell metabolism has been shown to play an active role in regulation of stemness and fate decision. In order to identify favorable culture conditions for mesenchymal stromal cells (MSCs) prior to transplantation, this study aimed to characterize the metabolic function of MSCs from different developmental stages in response to different oxygen tension during expansion. Materials and methods We cultured human fetal cardiac MSCs and human adult bone-marrow MSCs for a week under hypoxia (3% O2) and normoxia (20% O2). We performed mitochondrial characterization and assessed oxygen consumption- and extracellular acidification-rates (OCR and ECAR) in addition to oxygen-sensitive respiration and mitochondrial complex activities, using both the Seahorse and Oroboros systems. Results Adult and fetal MSCs displayed similar basal respiration and mitochondrial amount, however fetal MSCs had lower spare respiratory capacity and apparent coupling efficiency. Fetal MSCs expanded in either hypoxia or normoxia demonstrated similar acidification rates, while adult MSCs downregulated their aerobic glycolysis in normoxia. Acute decrease in oxygen tension caused a higher respiratory inhibition in adult compared to fetal MSCs. In both sources of MSCs, minor changes in complex activities in normoxic and hypoxic cultures were found. Conclusions In contrast to adult MSCs, fetal MSCs displayed similar respiration and aerobic glycolysis at different O2 culture concentrations during expansion. Adult MSCs adjusted their respiration to glycolytic activities, depending on the culture conditions thus displaying a more mature metabolic function. These findings are relevant for establishing optimal in vitro culturing conditions, with the aim to maximize engraftment and therapeutic outcome. CC BY-NC-ND 4.0Corresponding author: Department of Surgical Sciences, Uppsala University, 751 85, Uppsala, Sweden. E-mail address: karl-henrik.grinnemo@surgsci.uu.se (K.-H. Grinnemo).Available online 3 February 2022, Version of Record 5 February 2022The project was funded by Karolinska Institute-Mayo Clinic Collaborative Grant 2013; The Swedish Research Council young investigator: 2013–3590; Stockholm county; The Swedish Research Council; The Family Erling-Persson Foundation; ERC-2018-AdG (834860 EYELETS); Uppsala county; Uppsala County Association against Heart and Lung Diseases; and Higher Education of the Russian Federation (agreement no. 075-15-2020-899).

Extracellular matrix (ECM) components govern a range of cell functions such as migration, proliferation, maintenance of stemness and differentiation. Cell niches that harbor stem-/progenitor cells, with matching ECM, have been shown in a range of organs, although their presence in the heart is still under debate. Determining niches depends on a range of in vitro and in vivo models and techniques, where animal models are powerful tools for studying cell-ECM dynamics, however, they are costly and time-consuming to use. In vitro models based on recombinant ECM proteins lack the complexity of the in vivo ECM. To address these issues, we present the Spatiotemporal Extracellular Matrix Model (StEMM) for studies of cell-ECM dynamics, such as cell niches. This model combines gentle decellularization and sectioning of cardiac tissue, allowing retention of a complex ECM, with recellularization and subsequent image processing using image stitching, segmentation, automatic binning and generation of cluster maps. We have thereby developed an in situ representation of the cardiac ECM that is useful for assessment of repopulation dynamics and to study the effect of local ECM composition on phenotype preservation of reseeded mesenchymal progenitor cells. This model provides a platform for studies of organ-specific cell-ECM dynamics and identification of potential cell niches. © AlphaMed Press 2021 SIGNIFICANCE STATEMENT: Stem cells reside in adult organs within specific microenvironments called cell niches. The heart is a complex organ and so far, the presence and localization of stem-/progenitor cell niches are subject to constant debate. To address these issues, the authors have developed the Spatiotemporal Extracellular Matrix Model (StEMM), which combines a modified protocol for decellularization, with cryo-sectioning, recellularization, and subsequent image processing including automatic binning and generation of cluster maps. StEMM was developed within a cardiac context and validated using syngeneic mesenchymal progenitor cells. However, this model is not restricted with regard to species or organs. CC BY-NC 4.0Correspondence: Karl-Henrik Grinnemo, MD, Department of Surgical Sciences, Division of Cardiothoracic Surgery, Uppsala University Hospital, SE-75185 Uppsala, Sweden. Email: karl-henrik.grinnemo@surgsci.uu.seFunding information: Uppsala Region (RuFu); Uppsala County Council (ALF); Swedish Research Council, Young Investigator, Grant/Award Number: 2013-03590

Cytokines and chemokines play a crucial role in regulating the immune system. Understanding how these molecules are co-regulated is important to understand general immunology, and particularly their role in clinical applications such as development and evaluation of novel drug therapies. Cytokines are today widely used as therapeutic targets and as biomarkers to monitor effects of drug therapies and for prognosis and diagnosis of diseases. Therapies that target a specific cytokine are also likely to affect the production of other cytokines due to their cross-regulatory functions and because the cytokines are produced by common cell types. In this study, we have perturbated the production of 17 different cytokines in a preclinical rat model of autoimmune arthritis, using 55 commercially available immunomodulatory drugs and clinical candidates. The majority of the studied drugs was selected for their anti-inflammatory role and was confirmed to inhibit the production of IL-2 and IFN-γ in this model but was also found to increase the production of other cytokines compared to the untreated control. Correlation analysis identified 58 significant pairwise correlations between the cytokines. The strongest correlations found in this study were between IL-2 and IFN-γ (r=0.87) and between IL-18 and EPO (r=0.84). Cluster analysis identified two robust clusters: (1) IL-7, IL-18 and EPO, and (2) IL-2, IL-17 and IFN-γ. The results show that cytokines are highly co-regulated, which provide valuable information for how a therapeutic drug might affect clusters of cytokines. In addition, a cytokine that is used as a therapeutic biomarker could be combined with its related cytokines into a biomarker panel to improve diagnostic accuracy.

Significance One approach to testing biological theories is to determine if they are predictive. We have developed a simple, theoretical treatment of T cell receptor (TCR) triggering that relies on just two physical principles: (i) the time TCRs spend in cell–cell contacts depleted of large tyrosine phosphatases and (ii) constraints on the size of these contacts imposed by cell topography. The theory not only distinguishes between agonistic and nonagonistic TCR ligands but predicts the relative signaling potencies of agonists with remarkable accuracy. These findings suggest that the theory captures the essential features of receptor triggering., The T cell receptor (TCR) initiates the elimination of pathogens and tumors by T cells. To avoid damage to the host, the receptor must be capable of discriminating between wild-type and mutated self and nonself peptide ligands presented by host cells. Exactly how the TCR does this is unknown. In resting T cells, the TCR is largely unphosphorylated due to the dominance of phosphatases over the kinases expressed at the cell surface. However, when agonist peptides are presented to the TCR by major histocompatibility complex proteins expressed by antigen-presenting cells (APCs), very fast receptor triggering, i.e., TCR phosphorylation, occurs. Recent work suggests that this depends on the local exclusion of the phosphatases from regions of contact of the T cells with the APCs. Here, we developed and tested a quantitative treatment of receptor triggering reliant only on TCR dwell time in phosphatase-depleted cell contacts constrained in area by cell topography. Using the model and experimentally derived parameters, we found that ligand discrimination likely depends crucially on individual contacts being ∼200 nm in radius, matching the dimensions of the surface protrusions used by T cells to interrogate their targets. The model not only correctly predicted the relative signaling potencies of known agonists and nonagonists but also achieved this in the absence of kinetic proofreading. Our work provides a simple, quantitative, and predictive molecular framework for understanding why TCR triggering is so selective and fast and reveals that, for some receptors, cell topography likely influences signaling outcomes.

Drug discovery is a constant struggle to overcome hurdles posed by the complexity of biological systems. One of these hurdles is to find and understand the molecular target and the biological mechanism of action. Although the molecular target has been determined, the true biological effect may be unforeseen also for well-established drugs. Hence, there is a need for novel ways to increase the knowledge of the biological effects of drugs in the developmental process. In this study, we have determined cytokine profiles for 26 non-biological immunomodulatory drugs or drug candidates and used these profiles to cluster the compounds according to their effect in a preclinical ex vivo culture model of arthritis. This allows for prediction of functions and drug target of a novel drug candidate based on profiles obtained in this study. Results from the study showed that the JAK inhibitors tofacitinib and ruxolitinib formed a robust cluster and were found to have a distinct cytokine profile compared to the other drugs. Another robust cluster included the calcineurin inhibitors cyclosporine A and tacrolimus and the protein kinase inhibitors fostamatinib disodium and sotrastaurin acetate, which caused a strong overall inhibition of the cytokine production. The results of this methodology indicate that cytokine profiles can be used to provide a fingerprint-like identification of a drug as a tool to benchmark novel drugs and to improve descriptions of mode of action.

Programmed cell death-1 (PD-1) is an inhibitory receptor with an essential role in maintaining peripheral tolerance and is among the most promising immunotherapeutic targets for treating cancer, autoimmunity, and infectious diseases. A complete understanding of the consequences of PD-1 engagement by its ligands, PD-L1 and PD-L2, and of PD-L1 binding to B7-1 requires quantitative analysis of their interactions at the cell surface. We present here the first complete in situ kinetic analysis of the PD-1/PD-ligands/B7-1 system. Consistent with previous solution measurements, we observed higher in situ affinities for human (h) than murine (m) PD-1 interactions, stronger binding of hPD-1 to hPD-L2 than hPD-L1, and comparable binding of mPD-1 to both ligands. However, in contrast to the relatively weak solution affinities, the in situ affinities of PD-1 are as high as those of the T cell receptor for agonist pMHC and of LFA-1 (lymphocyte function-associated antigen 1) for ICAM-1 (intercellular adhesion molecule 1) but significantly lower than that of the B7-1/CTLA-4 interaction, suggesting a distinct basis for PD-1- versus CTLA-4-mediated inhibition. Notably, the in situ interactions of PD-1 are much stronger than that of B7-1 with PD-L1. Overall, the in situ affinity ranking greatly depends on the on-rate instead of the off-rate. In silico simulations predict that PD-1/PD-L1 interactions dominate at interfaces between activated T cells and mature dendritic cells and that these interactions will be highly sensitive to the dynamics of PD-L1 and PD-L2 expression. Our results provide a kinetic framework for better understanding inhibitory PD-1 activity in health and disease. CC BY 4.0