No abstract

The meninges contain adaptive immune cells that provide immunosurveillance of the CNS. These cells are thought to derive from the systemic circulation. Through single-cell analyses, confocal imaging, bone marrow chimeras, and parabiosis experiments, we show that meningeal B cells derive locally from the calvaria, which harbors a bone marrow niche for hematopoiesis. B cells reach the meninges from the calvaria through specialized vascular connections. This calvarial–meningeal path of B cell development may provide the CNS with a constant supply of B cells educated by CNS antigens. Conversely, we show that a subset of antigen-experienced B cells that populate the meninges in aging mice are blood-borne. These results identify a private source for meningeal B cells. which may help maintain immune privilege within the CNS.

Epigenetic clocks are widely used aging biomarkers calculated from DNA methylation data. Unfortunately, measurements for individual CpGs can be surprisingly unreliable due to technical noise, and this may limit the utility of epigenetic clocks. We report that noise produces deviations up to 3 to 9 years between technical replicates for six major epigenetic clocks. The elimination of low-reliability CpGs does not ameliorate this issue.Here, we present a novel computational multi-step solution to address this noise, involving performing principal component analysis on the CpG-level data followed by biological age prediction using principal components as input. This method extracts shared systematic variation in DNAm while minimizing random noise from individual CpGs. Our novel principal-component versions of six clocks show agreement between most technical replicates within 0 to 1.5 years, equivalent or improved prediction of outcomes, and more stable trajectories in longitudinal studies and cell culture. This method entails only one additional step compared to traditional clocks, does not require prior knowledge of CpG reliabilities, and can improve the reliability of any existing or future epigenetic biomarker. The high reliability of principal component-based epigenetic clocks will make them particularly useful for applications in personalized medicine and clinical trials evaluating novel aging interventions..

Robust biomarkers of aging have been developed from DNA methylation in humans and more recently, in mice. This study aimed to generate a novel epigenetic clock in rats—a model with unique physical, physiological, and biochemical advantages—by incorporating behavioral data, unsupervised machine learning, and network analysis to identify epigenetic signals that not only track with age, but also relates to phenotypic aging. Reduced representation bisulfite sequencing (RRBS) data was used to train an epigenetic age (DNAmAge) measure in Fischer 344 CDF (F344) rats. This measure correlated with age at (r = 0.93) in an independent sample, and related to physical functioning (p=5.9e-3), after adjusting for age and cell counts. DNAmAge was also found to correlate with age in male C57BL/6 mice (r = 0.79), and was decreased in response to caloric restriction. Our signatures driven by CpGs in intergenic regions that showed substantial overlap with H3K9me3, H3K27me3, and E2F1 transcriptional factor binding.

Epigenetic clocks are widely used aging biomarkers calculated from DNA methylation data. Unfortunately, measurements for individual CpGs can be surprisingly unreliable due to technical noise, and this may limit the utility of epigenetic clocks. We report that noise produces deviations up to 3 to 9 years between technical replicates for six major epigenetic clocks. The elimination of low-reliability CpGs does not ameliorate this issue. Here, we present a novel computational multi-step solution to address this noise, involving performing principal component analysis on the CpG-level data followed by biological age prediction using principal components as input. This method extracts shared systematic variation in DNAm while minimizing random noise from individual CpGs. Our novel principal-component versions of six clocks show agreement between most technical replicates within 0 to 1.5 years, equivalent or improved prediction of outcomes, and more stable trajectories in longitudinal studies and cell culture. This method entails only one additional step compared to traditional clocks, does not require prior knowledge of CpG reliabilities, and can improve the reliability of any existing or future epigenetic biomarker. The high reliability of principal component-based epigenetic clocks will make them particularly useful for applications in personalized medicine and clinical trials evaluating novel aging interventions.

Single-cell RNA-sequencing data has revolutionized our ability to understand of the patterns of cell–cell and ligand–receptor connectivity that influence the function of tissues and organs. However, the quantification and visualization of these patterns in a way that informs tissue biology are major computational and epistemological challenges. Here, we present Connectome, a software package for R which facilitates rapid calculation and interactive exploration of cell–cell signaling network topologies contained in single-cell RNA-sequencing data. Connectome can be used with any reference set of known ligand–receptor mechanisms. It has built-in functionality to facilitate differential and comparative connectomics, in which signaling networks are compared between tissue systems. Connectome focuses on computational and graphical tools designed to analyze and explore cell–cell connectivity patterns across disparate single-cell datasets and reveal biologic insight. We present approaches to quantify focused network topologies and discuss some of the biologic theory leading to their design.

CD19-specific chimeric antigen receptor (CAR) T cell therapies have been highly effective against B cell malignancies. We previously demonstrated that differential responses to anti-CD19 CAR T cell therapy in chronic lymphocytic leukemia (CLL) are associated with early memory T cell signature in apheresed, pre-manufacturing T-cells (CAR T-cell precursors). We tested the hypothesis that the composition of CAR-T precursor cells determines clinical efficacy in adult and pediatric Acute Lymphoblastic Leukemia (ALL), Non-Hodgkin's Lymphoma (NHL), Multiple Myeloma (MM), and CLL. Apheresed T cells were engineered to express 4-1BB plus CD3-zeta-signaling CARs targeting CD19, or B cell maturation antigen (BCMA). The same 9-day manufacturing process was used for all trials. CAR T cell kinetics were monitored using a CAR gene-specific quantitative PCR assay and standard clinical response assessments were performed. Apheresed T cells from 36 CLL, 30 adult ALL, 58 pediatric ALL, 33 NHL, and 25 MM patients were immunophenotyped by flow cytometry. The CLL cohort was used to discover phenotypically distinct subpopulations associated with the two main response groups; these associations were validated in the remaining patient cohorts. Eight CD8+ T cell populations or clusters were identified using the shared-nearest-neighbor clustering method (PMID: 31178118) in the CLL cohort. T cell subsets exhibiting naive (cluster 6) or early memory (cluster 4) features were significantly enriched in responding patients, whereas an effector memory CD8 subpopulation (cluster 2) marked the non-responding patients. Mapping these clusters onto apheresed CD8+ T cells from the other four diseases showed that cluster 4 predicted response to CAR T cell therapy in NHL and myeloma but not in adult and pediatric ALL. We also examined the expression of activation-regulated molecules including HLA-DR, Ki67, and exhaustion-related molecules PD1, CTLA4, TIM3, and LAG3. A CD27+ CD8+ population expressing low level CTLA4 but none of the activation or negative regulatory molecules was significantly enriched in responding CLL patients; this cluster validated in NHL and myeloma. A similar analysis on apheresed CD4+ T cells identified an early memory population (cluster 6) enriched in CLL responders, which expresses CCR7 and CD27 but not CD45RO, CD127, CD28, or other late memory/effector molecules. However, this population did not validate in any of the other diseases. Though not statistically significant, the CD4+ clusters with the largest effect size for enrichment in responders from NHL and myeloma trials exhibited early memory T cell features and lack of HLA-DR expression, suggesting that quiescent early memory state in CD4 may also be associated with clinical responses. A separate analysis of checkpoint inhibitory receptors and activation markers in memory CD4 T cell subsets confirmed the early memory, non-activated state of this population in CLL and was validated in myeloma but none of the other diseases. In vivo activation was a shared theme in CD4+ T cells for non-responding patients as well, though these CLL-defined CD4+ apheresed T cells clusters did not significantly validate in other diseases. In summary, our data confirm and extend our predictive biomarker profile in CLL to mature B cell and plasma cell malignancies by showing that a non-cycling, non-activated early memory CD8+ T cell population in pre-manufacturing cells was validated as a biomarker in myeloma, and NHL. We also showed that responder-associated apheresed CD4+ T cells with early memory features identified in CLL after CD19 CAR T infusions are validated in myeloma after BCMA CAR T. Thus, differentiation state and in vivo activation, and potentially exhaustion, separate response groups. Our findings inform next-generation CAR T-cell manufacturing using the populations identified herein as a starting population. Disclosures Pruteanu: Novartis: Employment. Cohen:Poseida Therapeutics, Inc.: Research Funding. Garfall:Surface Oncology: Consultancy; Novartis: Research Funding; Janssen: Research Funding; Amgen: Research Funding; Tmunity: Research Funding. Milone:Novartis: Patents & Royalties: patents related to tisagenlecleucel (CTL019) and CART-BCMA; Novartis: Research Funding. Gill:Novartis: Research Funding; Tmunity: Research Funding; Carisma: Equity Ownership, Research Funding; Sensei: Consultancy; Aro: Consultancy; Fate: Consultancy. Frey:Novartis: Research Funding. Ruella:Nanostring: Consultancy, Speakers Bureau; Novartis: Patents & Royalties: CART for cancer; AbClon: Membership on an entity's Board of Directors or advisory committees. Lacey:Novartis: Patents & Royalties: Patents related to CAR T cell biomarkers; Tmunity: Research Funding; Novartis: Research Funding. Svoboda:Merck: Research Funding; BMS: Consultancy, Research Funding; Incyte: Research Funding; Pharmacyclics: Consultancy, Research Funding; Celgene: Research Funding; Kite: Consultancy; Seattle Genetics: Consultancy, Research Funding; Kyowa: Consultancy; AstraZeneca: Consultancy. Chong:Tessa: Consultancy; Novartis: Consultancy; Merck: Research Funding. Fraietta:LEK Consulting: Consultancy; Cabaletta: Research Funding; Tmunity: Research Funding. Davis:Cabaletta: Research Funding; Tmunity: Research Funding. Nasta:Rafael: Research Funding; Aileron: Research Funding; Takeda/Millennium: Research Funding; Incyte: Research Funding; Roche/Genentech: Research Funding; Merck: Consultancy; Atara: Research Funding; Debiopharm: Research Funding. Levine:CRC Oncology: Consultancy; Vycellix: Membership on an entity's Board of Directors or advisory committees; Tmunity Therapeutics: Equity Ownership; Novartis: Consultancy, Patents & Royalties, Research Funding; Cure Genetics: Consultancy; Avectas: Membership on an entity's Board of Directors or advisory committees; Brammer Bio: Membership on an entity's Board of Directors or advisory committees; Incysus: Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy. Maude:Kite: Consultancy; Novartis: Consultancy. Schuster:Nordic Nanovector: Honoraria; Pfizer: Honoraria; AstraZeneca: Honoraria; Pharmacyclics: Honoraria, Research Funding; Genentech: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Loxo Oncology: Honoraria; Merck: Honoraria, Research Funding; Acerta: Honoraria, Research Funding; Novartis: Honoraria, Patents & Royalties: Combination Therapies of CAR and PD-1 Inhibitors with royalties paid to Novartis, Research Funding; AbbVie: Honoraria, Research Funding; Gilead: Honoraria, Research Funding. Stadtmauer:Celgene: Consultancy; Tmunity: Research Funding; Novartis: Consultancy, Research Funding; Takeda: Consultancy; Janssen: Consultancy; Amgen: Consultancy; Abbvie: Research Funding. Grupp:Novartis: Consultancy, Research Funding; Roche: Consultancy; GSK: Consultancy; Cure Genetics: Consultancy; Humanigen: Consultancy; CBMG: Consultancy; Novartis: Research Funding; Kite: Research Funding; Servier: Research Funding; Jazz: Other: study steering committees or scientific advisory boards; Adaptimmune: Other: study steering committees or scientific advisory boards. Porter:Incyte: Membership on an entity's Board of Directors or advisory committees; American Board of Internal Medicine: Membership on an entity's Board of Directors or advisory committees; Kite: Membership on an entity's Board of Directors or advisory committees; Glenmark Pharm: Membership on an entity's Board of Directors or advisory committees; Immunovative: Membership on an entity's Board of Directors or advisory committees; Genentech: Employment; Wiley and Sons: Honoraria; Novartis: Membership on an entity's Board of Directors or advisory committees, Patents & Royalties, Research Funding. June:Novartis: Research Funding; Tmunity: Other: scientific founder, for which he has founders stock but no income, Patents & Royalties. Melenhorst:Novartis: Research Funding, Speakers Bureau; Parker Institute for Cancer Immunotherapy: Research Funding; Stand Up to Cancer: Research Funding; Incyte: Research Funding; IASO Biotherapeutics, Co: Consultancy; Simcere of America, Inc: Consultancy; Shanghai Unicar Therapy, Co: Consultancy; Colorado Clinical and Translational Sciences Institute: Membership on an entity's Board of Directors or advisory committees; Genentech: Speakers Bureau; National Institutes of Health: Research Funding.

No abstract