Jichun Xie

Alloreactivity can drive autoimmune syndromes. After allogeneic hematopoietic stem cell transplantation (allo-HCT) chronic graft-versus-host disease (cGVHD), a B cell-mediated autoimmune-like syndrome, commonly occurs. Because donor-derived B cells continually develop under selective pressure from host alloantigens, aberrant B Cell Receptor (BCR)-activation and IgG production can emerge and contribute to cGVHD pathobiology. To better understand molecular programing of B cells under selective pressure of alloantigens, we performed scRNA-Seq analysis on high numbers of purified B cells from allo-HCT patients. An unsupervised analysis revealed 10 clusters, distinguishable by signature genes for maturation, activation and memory. We found striking transcriptional differences in the memory B cell compartment after allo-HCT compared to healthy or infected individuals. To identify intrinsic properties when B-cell tolerance is lost after allo-HCT, we then assessed clusters for differentially expressed genes (DEGs) between patients with vs. without autoimmune-like manifestations (Active cGVHD vs. No cGVHD, respectively). DEGs were found in Active cGVHD in both naive and BCR-activated clusters, suggesting functional diversity. Some DEGs were also differentially expressed across most clusters, suggesting common molecular programs that may promote B cell plasticity. Our study of human allo-HCT and cGVHD provides new understanding of B-cell memory in the face of chronic alloantigen stimulation.

OBJECTIVES: To characterize and quantify accumulating immunological alterations, pre- and post-operatively in patients undergoing elective surgical procedures. SUMMARY BACKGROUND DATA: Elective surgery is an anticipatable, controlled human injury. Although the human response to injury is generally stereotyped, individual variability exists. This makes surgical outcomes less predictable, even after standardized procedures, and may provoke complications in patients unable to compensate for their injury. One potential source of variation is found in immune cell maturation, with phenotypic changes dependent on an individual's unique, lifelong response to environmental antigens. METHODS: We enrolled 248 patients in a prospective trial facilitating comprehensive biospecimen and clinical data collection in patients scheduled to undergo elective surgery. Peripheral blood was collected pre-operatively, and immediately upon return to the post- anesthesia care unit. Postoperative complications that occurred within 30 days after surgery were captured. RESULTS: As this was an elective surgical cohort, outcomes were generally favorable. With a median follow-up of 6 months, the overall survival at 30 days was 100%. However, 20.5% of the cohort experienced a postoperative complication (infection, readmission, or system dysfunction). We identified substantial heterogeneity of immune senescence and terminal differentiation phenotypes in surgical patients. More importantly, phenotypes indicating increased T-cell maturation and senescence were associated with postoperative complications and were evident pre-operatively. CONCLUSIONS: The baseline immune repertoire may define an immune signature of resilience to surgical injury and help predict risk for surgical complications.

In immunology studies, flow cytometry is a commonly used multivariate single-cell assay. One key goal in flow cytometry analysis is to detect the immune cells responsive to certain stimuli. Statistically, this problem can be translated into comparing two protein expression probability density functions (PDFs) before and after the stimulus; the goal is to pinpoint the regions where these two PDFs differ. Further screening of these differential regions can be performed to identify enriched sets of responsive cells. In this paper we model identifying differential density regions as a multiple testing problem. First, we partition the sample space into small bins. In each bin we form a hypothesis to test the existence of differential PDFs. Second, we develop a novel multiple testing method, called TEAM (testing on the aggregation tree method), to identify those bins that harbor differential PDFs while controlling the false discovery rate (FDR) under the desired level. TEAM embeds the testing procedure into an aggregation tree to test from fine-to coarse-resolution. The procedure achieves the statistical goal of pinpointing density differences to the smallest possible regions. TEAM is computationally efficient, capable of analyzing large flow cytometry data sets in much shorter time compared with competing methods. We applied TEAM and competing methods on a flow cytometry data set to identify T cells responsive to the cytomegalovirus (CMV)-pp65 antigen stimulation. With additional downstream screening, TEAM successfully identified enriched sets containing monofunctional, bifunctional, and polyfunctional T cells. Competing methods either did not finish in a reasonable time frame or provided less interpretable results. Numerical simulations and theoretical justifications demonstrate that TEAM has asymptotically valid, powerful, and robust performance. Overall, TEAM is a computationally efficient and statistically powerful algorithm that can yield meaningful biological insights in flow cytometry studies.

This study sought to identify neuroimaging and immunological factors associated with substance use and that contribute to neurocognitive impairment (NCI) in people with HIV (PWH). We performed cross-sectional immunological phenotyping, neuroimaging, and neurocognitive testing on virally suppressed PWH in four substance groups: cocaine only users (COC), marijuana only users (MJ), dual users (Dual), and Non-users. Participants completed substance use assessments, multimodal MRI brain scan, neuropsychological testing, and blood and CSF sampling. We employed a two-stage analysis of 305 possible biomarkers of cognitive function associated with substance use. Feature reduction (Kruskal Wallis p-value < 0.05) identified 53 biomarkers associated with substance use (22 MRI and 31 immunological) for model inclusion along with clinical and demographic variables. We employed eXtreme Gradient Boosting (XGBoost) with these markers to predict cognitive function (global T-score). SHapley Additive exPlanations (SHAP) values were calculated to rank features for impact on model output and NCI. Participants were 110 PWH with sustained HIV viral suppression (33 MJ, 12 COC, 22 Dual, and 43 Non-users). The ten highest ranking biomarkers for predicting global T-score were 4 neuroimaging biomarkers including functional connectivity, gray matter volume, and white matter integrity; 5 soluble biomarkers (plasma glycine, alanine, lyso-phosphatidylcholine (lysoPC) aC17.0, hydroxy-sphingomyelin (SM.OH) C14.1, and phosphatidylcholinediacyl (PC aa) C28.1); and 1 clinical variable (nadir CD4 count). The results of our machine learning model suggest that substance use may indirectly contribute to NCI in PWH through both metabolomic and neuropathological mechanisms.

Cells respond to many stressors by senescing, acquiring stable growth arrest, morphologic and metabolic changes, and a proinflammatory senescence-associated secretory phenotype. The heterogeneity of senescent cells (SnCs) and senescence-associated secretory phenotype are vast, yet ill characterized. SnCs have diverse roles in health and disease and are therapeutically targetable, making characterization of SnCs and their detection a priority. The Cellular Senescence Network (SenNet), a National Institutes of Health Common Fund initiative, was established to address this need. The goal of SenNet is to map SnCs across the human lifespan to advance diagnostic and therapeutic approaches to improve human health. State-of-the-art methods will be applied to identify, define and map SnCs in 18 human tissues. A common coordinate framework will integrate data to create four-dimensional SnC atlases. Other key SenNet deliverables include innovative tools and technologies to detect SnCs, new SnC biomarkers and extensive public multi-omics datasets. This Perspective lays out the impetus, goals, approaches and products of SenNet.