Abstract: Cell-cell communication is essential in healthy bone marrow (BM) and seems to be disturbed in acute myeloid leukemia (AML). To understand whether the communication patterns are different in healthy and malignant BM, we need to perform a comparative analysis--a task which most algorithms published to date are not designed to do. We solved this issue with our new computational tool which analyses differential communication in two cohorts of multiple samples and allows us to do systematic studies of communication changes in large cohorts of individuals. Our algorithm works on single-cell RNA-seq or bulk RNA-seq data of individual FACS-sorted cell types. Here we use two published datasets of human bone marrow single-cell RNA-seq containing samples from AML patients at diagnosis, as well as healthy donors. We selected 9 AML samples from van Galen, 2019 by excluding samples collected later than day 0 after diagnosis, having less than 50% blasts at diagnosis, or having less than 5 cell types captured. As a control group, we used 29 samples from healthy donors (4 from van Galen, 2019 and 25 from Oetjen, 2018). To unify the cell type annotation, we merged the original cell type annotation into bigger classes: hematopoietic stem and progenitor cells (HSPCs), monocytes, erythrocytes, B cells, T cells, natural killer cells (NK), and dendritic cells (DC). Due to low capturing rate, we excluded NK from the subsequent analysis. After filtering out lowly expressed genes and cells with low number of reads, we normalised the data using scran package (Lun, 2016). We next proceeded to calculate the communication between the 6 cell types for each sample. A communication edge represents a signal which is sent by a "sending" cell type via its ligand and is received by a "receiving" cell type via its receptor. Each edge has a weight that indicates the intensity of communication and varies between 0 (no communication) and 1 (communication of maximum intensity). We calculate edge weights using a formula which addresses the proportion of the cells within each cell type that expressed the ligand/receptor, the relative level of expression of ligand/receptor in sending/receiving cell type compared to other cell types and the discrepancy of expression between the ligand in the sending cell type and the receptor in the receiving cell type. We used iTALK (Wang, 2019) database of ligand-receptor pair interactions as a reference. After calculating communication edges in all samples, we filter low-weight edges and proceed to differential communication analysis. Here we perform Wilcoxon-Mann-Whitney test with Bonferroni correction in the two groups of samples (AML vs healthy). We identified over 4.500 communication edges in the dataset, of which 58 were significantly differentially used (adjusted p-value & lt; 0.1). Markedly, the majority of the communication edges, as well as all significantly differential edges were down-regulated in the AML samples, indicating overall decrease in communicative activity among the investigated cell types in AML patients at diagnosis. In the significantly differential edges -- which were actively used in the healthy bone marrow, but lost in the AML -- the two most active "sending" cell types were the erythrocytes (in 19 edges) and monocytes (in 17 edges), followed by the DC (in 9 edges), B cells (in 7 edges), T cells (in 4 edges), and HSCP (in 2 edges). The most active receiving cell types were the DC (in 24 edges) and the erythrocyte (in 17 edges), followed by the monocytes (in 8 edges), B cells (in 6 edges), HSCP (in 2 edges) and T cells (in 1 edge). The top 20 significantly differential edges showed communication from erythrocytes, monocytes, B cells and T cells to DC using CALM1/3 : SELL, as well as ADAM10 : AXL interaction, indicating reduced cell-cell adhesive contact among the immune cell types in the AML. In our study, we developed a computational tool which allows us to characterize differential cell-cell communication in two cohorts of patients using scRNA-seq data. We applied our tool to the published scRNA-seq data from the bone marrow of 8 AML patients and 29 healthy donors. We identified disease-driven loss in communication patterns in the AML such as disrupted adhesion among the immune cells, which might indicate immunoevasion. This analysis is crucial to generate new data driven hypotheses for a deeper understanding of haematopoietic neoplasms. Disclosures No relevant conflicts of interest to declare.

Abstract: Follicular lymphoma (FL) is a clinically and genetically heterogeneous disease. Somatic gene mutations contribute to the heterogeneous clinical course of FL. ARID1A, which encodes for a subunit of the SWI/SNF chromatin remodeling complex, is among the most commonly mutated genes in FL (up to 15% of cases). These mutations are mostly disruptive and are predicted to result in protein haplodeficiency. While we have previously shown that ARID1A mutations are predictive of treatment outcome (Pastore, 2015), the underlying biology of ARID1A loss in FL is unclear. A functional genome-wide in vitro screen showed that ARID1A loss rescued a number of cancer cell lines from FAS-L induced apoptosis (Luo, 2008). FAS-L induced apoptosis plays a critical role in normal B-cell development and homeostasis. Thus, FAS/FAS-L deficiency could contribute to FL development and disease biology. Therefore, we studied the role of ARID1A loss in FAS expression and regulation. We first tested FAS-L induced apoptosis in established lymphoma cell lines that harbor the FL-hallmark translocation t(14;18)[BCL2/IGH] plus ARID1A mutations (Karpas422, WSU-FSCCL) or no ARID1A mutations (OCI-Ly1, OCI-Ly8, SU-DHL16). ARID1A mutant (mut) cells were indeed markedly less sensitive to FAS-L (300 ng/mL/24 hrs) compared to ARID1A wild type (WT) cells (98% vs 52% mean viability by Annexin-V). FAS receptor expression on mutant cells was reduced by almost half compared to WT cells by FACS analysis (N=3, P=0.0004). To test if reduced FAS expression was directly linked to ARID1A loss, we generated single-cell derived clones (from OCI-Ly1 and OCI-Ly8) with either heterozygous (het) loss or complete knock-out (KO) of ARID1A by CRISPR/Cas9. ARID1A loss was validated by Sanger sequencing and Western blot. We consistently observed significantly reduced FAS-L induced apoptosis in het and KO clones (exemplary shown for OCI-Ly8 in Fig A). Remarkably, re-expressed of ARID1A in het cells (het+ARID1A) rescued sensitivity to FAS-L induced apoptosis (Fig A). We confirmed reduced FAS expression on mutant clones by FACS, while re-expression of ARID1A rescued its expression (Fig B). Furthermore, FAS mRNA expression was significantly reduced by qPCR in mut vs WT clones (N=4, P & lt;0.05), while FAS mRNA levels were rescued to WT levels in het+ARID1A cells. To understand the molecular mechanism that links ARID1A loss and reduced FAS expression, we performed ATAC sequencing (Seq) and RNA Seq on 15 single-cell derived clones (9 mut and 6 WT from OCI-Ly1 and OCI-Ly8). RNA Seq confirmed significantly lower ARID1A and FAS mRNA levels (adj p & lt;0.001 each) in the mut clones. We first hypothesized that ARID1A loss could directly affect chromatin accessibility at the FAS promoter. However, we did not observe different chromatin accessibility at the FAS promoter. Next, we searched our data for all known FAS-regulating transcription factors (TFs) (https://dorothea.opentargets.io/#/), but could not identify candidates that were both differentially accessible and differentially expressed. Finally, we searched our data for transcriptional networks, i.e. hubs of all recognized FAS-regulating TFs and their known and predicted interacting partners (https://string-db.org/). Through this, we identified RUNX3, a predicted Co-TF of ETS1, to be both less accessible ("closed chromatin") and less expressed upon ARID1A loss (Fig C), suggesting a novel ARID1A-dependent FAS-regulatory network. To functionally validate our model, we first confirmed reduced RUNX3 expression in ARID1A mutant clones by qPCR and Western blot, and showed that ETS1 levels were unaffected by ARID1A loss. Then, we stably overexpressed RUNX3 in ARID1A mutant clones by lentiviral transduction and could indeed show rescue of FAS surface levels by FACS (Fig D). Lastly, we wanted to validate our findings in primary patients samples. We quantified FAS expression in FL biopsies with known ARID1A mutation status by nCounter gene expression profiling (GEP; N=51, 12 mut vs 39 WT) and quantitative multispectral imaging (QMI; N=44, 10 mut vs 34 WT) (Fig E). Both approaches showed significantly reduced FAS expression in ARID1A mutant FL (P & lt;0.05 for GEP, P & lt;0.0001 for QMI; Fig E). In summary, we show that ARID1A loss is directly linked to reduced FAS expression via a novel RUNX3/ETS1 transcriptional network, potentially opening avenues for therapeutic targeting of this clinically relevant perturbation. Figure 1 Figure 1. Disclosures Subklewe: Pfizer: Consultancy, Speakers Bureau; Takeda: Speakers Bureau; Klinikum der Universität München: Current Employment; Janssen: Consultancy; Seattle Genetics: Consultancy, Research Funding; Roche: Research Funding; Novartis: Consultancy, Research Funding, Speakers Bureau; MorphoSys: Research Funding; Miltenyi: Research Funding; Gilead: Consultancy, Research Funding, Speakers Bureau; Amgen: Consultancy, Research Funding, Speakers Bureau; BMS/Celgene: Consultancy, Research Funding, Speakers Bureau. von Bergwelt: Kite/Gilead: Honoraria, Research Funding, Speakers Bureau; Roche: Honoraria, Research Funding, Speakers Bureau; Novartis: Honoraria, Research Funding, Speakers Bureau; Astellas: Honoraria, Research Funding, Speakers Bureau; Miltenyi: Honoraria, Research Funding, Speakers Bureau; BMS: Honoraria, Research Funding, Speakers Bureau; Mologen: Honoraria, Research Funding, Speakers Bureau; MSD Sharpe & Dohme: Honoraria, Research Funding, Speakers Bureau. Weigert: Janssen: Speakers Bureau; Epizyme: Membership on an entity's Board of Directors or advisory committees; Roche: Research Funding.

Abstract: Intercellular communication plays an essential role in multicellular organisms and several algorithms to analyze it from single-cell transcriptional data have been recently published, but the results are often hard to visualize and interpret. Results We developed Cell cOmmunication exploration with MUltiplex NETworks (COMUNET), a tool that streamlines the interpretation of the results from cell–cell communication analyses. COMUNET uses multiplex networks to represent and cluster all potential communication patterns between cell types. The algorithm also enables the search for specific patterns of communication and can perform comparative analysis between two biological conditions. To exemplify its use, here we apply COMUNET to investigate cell communication patterns in single-cell transcriptomic datasets from mouse embryos and from an acute myeloid leukemia patient at diagnosis and after treatment. Availability and implementation Our algorithm is implemented in an R package available from https://github.com/ScialdoneLab/COMUNET, along with all the code to perform the analyses reported here. Supplementary information Supplementary data are available at Bioinformatics online.

Abstract: Clonal hematopoiesis (CH) is an age-related condition predisposing to blood cancer and cardiovascular disease (CVD). Murine models demonstrate CH-mediated altered immune function and proinflammation. Low-grade inflammation has been implicated in the pathogenesis of osteoarthritis (OA), the main indication for total hip arthroplasty (THA). THA-derived hip bones serve as a major source of healthy hematopoietic cells in experimental hematology. We prospectively investigated frequency and clinical associations of CH in 200 patients without known hematologic disease who were undergoing THA. Prevalence of CH was 50%, including 77 patients with CH of indeterminate potential (CHIP, defined as somatic variant allele frequencies [VAFs] ≥2%), and 23 patients harboring CH with lower mutation burden (VAF, 1% to 2%). Most commonly mutated genes were DNMT3A (29.5%), TET2 (15.0%), and ASXL1 (3.5%). CHIP is significantly associated with lower hemoglobin, higher mean corpuscular volume, previous or present malignant disease, and CVD. Strikingly, we observed a previously unreported association of CHIP with autoimmune diseases (AIDs; multivariable adjusted odds ratio, 6.6; 95% confidence interval, 1.7-30; P = .0081). These findings underscore the association between CH and inflammatory diseases. Our results have considerable relevance for managing patients with OA and AIDs or mild anemia and question the use of hip bone–derived cells as healthy experimental controls.

Abstract: To implement and optimize a new approach for susceptibility‐weighted image (SWI) generation from multi‐echo multi‐channel image data and compare its performance against optimized traditional SWI pipelines. Materials and Methods Five healthy volunteers were imaged at 7 Tesla. The inter‐echo‐variance (IEV) channel combination, which uses the variance of the local frequency shift at multiple echo times as a weighting factor during channel combination, was used to calculate multi‐echo local phase shift maps. Linear phase masks were combined with the magnitude to generate IEV‐SWI. The performance of the IEV‐SWI pipeline was compared with that of two accepted SWI pipelines–channel combination followed by (i) Homodyne filtering (HPH‐SWI) and (ii) unwrapping and high‐pass filtering (SVD‐SWI). The filtering steps of each pipeline were optimized. Contrast‐to‐noise ratio was used as the comparison metric. Qualitative assessment of artifact and vessel conspicuity was performed and processing time of pipelines was evaluated. Results The optimized IEV‐SWI pipeline (σ = 7 mm) resulted in continuous vessel visibility throughout the brain. IEV‐SWI had significantly higher contrast compared with HPH‐SWI and SVD‐SWI ( P   〈  0.001, Friedman nonparametric test). Residual background fields and phase wraps in HPH‐SWI and SVD‐SWI corrupted the vessel signal and/or generated vessel‐mimicking artifact. Optimized implementation of the IEV‐SWI pipeline processed a six‐echo 16‐channel dataset in under 10 min. Conclusion IEV‐SWI benefits from channel‐by‐channel processing of phase data and results in high contrast images with an optimal balance between contrast and background noise removal, thereby presenting evidence of importance of the order in which postprocessing techniques are applied for multi‐channel SWI generation. Level of Evidence: 2 J. Magn. Reson. Imaging 2017;45:1113–1124