Quantitative measurement of alterations in DNA damage repair (DDR) pathways using single cell network profiling (SCNP).

BACKGROUND: Homologous recombination repair (HRR) pathway deficiencies have significant implications for cancer predisposition and treatment strategies. Improved quantitative methods for functionally characterizing these deficiencies are required to accurately identify patients at risk of developing cancer and to identify mechanisms of drug resistance or sensitivity. METHODS: Flow cytometry-based single cell network profiling (SCNP) was used to measure drug-induced activation of DNA damage response (DDR) proteins in cell lines with defined HRR pathway mutations (including ATM-/-, ATM+/-, BRCA1+/-, BRCA2-/-) and in primary acute myeloid leukemia (AML) samples. Both non-homologous end joining (NHEJ) and HRR pathways were examined by measuring changes in intracellular readouts (including p-H2AX, p-ATM, p-DNA-PKcs, p-53BP1, p-RPA2/32, p-BRCA1, p-p53, and p21) in response to exposure to mechanistically distinct genotoxins. The cell cycle S/G2/M phase CyclinA2 marker was used to normalize for proliferation rates. RESULTS: Etoposide induced proliferation-independent DNA damage and activation of multiple DDR proteins in primary AML cells and ATM +/+but not ATM -/- cell lines. Treatment with the PARPi AZD2281 +/- temozolomide induced DNA damage in CyclinA2+ cells in both primary AML cells and cell lines and distngiushed cell lines deficient (BRCA2-/-) or impaired (BRCA1+/-) in HRR activity from BRCA1+/+ cell lines based on p-H2AX induction. Application of this assay to primary AML samples identified heterogeneous patterns of repair activity including muted or proficient activation of NHEJ and HRR pathways and predominant activation of NHEJ in a subset of samples. CONCLUSIONS: SCNP identified functional DDR readouts in both NHEJ and HRR pathways, which can be applied to identify cells with BRCA1+/- haploinsuffiency and characterize differential DDR pathway functionality in primary clinical samples.

High-dimensional analysis of the aging immune system: verification of age-associated differences in immune signaling responses in healthy donors.

BACKGROUND: Single-cell network profiling (SCNP) is a multiparametric flow cytometry-based approach that simultaneously measures evoked signaling in multiple cell subsets. Previously, using the SCNP approach, age-associated immune signaling responses were identified in a cohort of 60 healthy donors. METHODS: In the current study, a high-dimensional analysis of intracellular signaling was performed by measuring 24 signaling nodes in 7 distinct immune cell subsets within PBMCs in an independent cohort of 174 healthy donors [144 elderly (>65 yrs); 30 young (25-40 yrs)]. RESULTS: Associations between age and 9 immune signaling responses identified in the previously published 60 donor cohort were confirmed in the current study. Furthermore, within the current study cohort, 48 additional immune signaling responses differed significantly between young and elderly donors. These associations spanned all profiled modulators and immune cell subsets. CONCLUSIONS: These results demonstrate that SCNP, a systems-based approach, can capture the complexity of the cellular mechanisms underlying immunological aging. Further, the confirmation of age associations in an independent donor cohort supports the use of SCNP as a tool for identifying reproducible predictive biomarkers in areas such as vaccine response and response to cancer immunotherapies.

Single-cell network profiling of peripheral blood mononuclear cells from healthy donors reveals age- and race-associated differences in immune signaling pathway activation.

A greater understanding of the function of the human immune system at the single-cell level in healthy individuals is critical for discerning aberrant cellular behavior that occurs in settings such as autoimmunity, immunosenescence, and cancer. To achieve this goal, a systems-level approach capable of capturing the response of the interdependent immune cell types to external stimuli is required. In this study, an extensive characterization of signaling responses in multiple immune cell subpopulations within PBMCs from a cohort of 60 healthy donors was performed using single-cell network profiling (SCNP). SCNP is a multiparametric flow cytometry-based approach that enables the simultaneous measurement of basal and evoked signaling in multiple cell subsets within heterogeneous populations. In addition to establishing the interindividual degree of variation within a broad panel of immune signaling responses, the possible association of any observed variation with demographic variables including age and race was investigated. Using half of the donors as a training set, multiple age- and race-associated variations in signaling responses in discrete cell subsets were identified, and several were subsequently confirmed in the remaining samples (test set). Such associations may provide insight into age-related immune alterations associated with high infection rates and diminished protection following vaccination and into the basis for ethnic differences in autoimmune disease incidence and treatment response. SCNP allowed for the generation of a functional map of healthy immune cell signaling responses that can provide clinically relevant information regarding both the mechanisms underlying immune pathological conditions and the selection and effect of therapeutics.

Serotonin-2 Receptor Agonists Produce Anti-inflammatory Effects through Functionally Selective Mechanisms That Involve the Suppression of Disease-Induced Arginase 1 Expression.

Functional selectivity in the context of serotonin 2A (5-HT2A) receptor agonists is often described as differences psychedelic compounds have in the activation of Gq vs ß-arrestin signaling in the brain and how that may relate to inducing psychoactive and hallucinatory properties with respect to each other. However, the presence of 5-HT2A receptors throughout the body in several cell types, including endothelial, endocrine, and immune-related tissues, suggests that functional selectivity may exist in the periphery as well. Here, we examine functional selectivity between two 5-HT2A receptor agonists of the phenylalkylamine class: (R)-2,5-dimethoxy-4-iodoamphetamine [(R)-DOI] and (R)-2,5-dimethoxy-4-trifluoromethylamphetamine [(R)-DOTFM]. Despite comparable in vitro activity at the 5-HT2A receptor as well as similar behavioral potency, (R)-DOTFM does not exhibit an ability to prevent inflammation or elevated airway hyperresponsiveness (AHR) in an acute murine ovalbumin-induced asthma model as does (R)-DOI. Furthermore, there are distinct differences between protein expression and inflammatory-related gene expression in pulmonary tissues between the two compounds. Using (R)-DOI and (R)-DOTFM as tools, we further elucidated the anti-inflammatory mechanisms underlying the powerful anti-inflammatory effects of certain psychedelics and identified key mechanistic components of the anti-inflammatory effects of psychedelics, including suppression of arginase 1 expression.

Development and validation of a single-cell network profiling assay-based classifier to predict response to induction therapy in paediatric patients with de novo acute myeloid leukaemia: a report from the Children's Oncology Group.

Single cell network profiling (SCNP) is a multi-parameter flow cytometry technique for simultaneous interrogation of intracellular signalling pathways. Diagnostic paediatric acute myeloid leukaemia (AML) bone marrow samples were used to develop a classifier for response to induction therapy in 53 samples and validated in an independent set of 68 samples. The area under the curve of a receiver operating characteristic curve (AUC(ROC)) was calculated to be 0·85 in the training set and after exclusion of induction deaths, the AUC(ROC) of the classifier was 0·70 (P = 0·02) and 0·67 (P = 0·04) in the validation set when induction deaths (intent to treat) were included. The highest predictive accuracy was noted in the cytogenetic intermediate risk patients (AUC(ROC) 0·88, P = 0·002), a subgroup that lacks prognostic/predictive biomarkers for induction response. Only white blood cell count and cytogenetic risk were associated with response to induction therapy in the validation set. After controlling for these variables, the SCNP classifier score was associated with complete remission (P = 0·017), indicating that the classifier provides information independent of other clinical variables that were jointly associated with response. This is the first validation of an SCNP classifier to predict response to induction chemotherapy. Herein we demonstrate the usefulness of quantitative SCNP under modulated conditions to provide independent information on AML disease biology and induction response.

Racial differences in B cell receptor signaling pathway activation.

BACKGROUND: Single-cell network profiling (SCNP) is a multi-parametric flow cytometry-based approach that simultaneously measures basal and modulated intracellular signaling activity in multiple cell subpopulations. Previously, SCNP analysis of a broad panel of immune signaling pathways in cell subsets within PBMCs from 60 healthy donors identified a race-associated difference in B cell anti-IgD-induced PI3K pathway activity. METHODS: The present study extended this analysis to a broader range of signaling pathway components downstream of the B cell receptor (BCR) in European Americans and African Americans using a subset of donors from the previously analyzed cohort of 60 healthy donors. Seven BCR signaling nodes (a node is defined as a paired modulator and intracellular readout) were measured at multiple time points by SCNP in PBMCs from 10 healthy donors [5 African Americans (36-51 yrs), 5 European Americans (36-56 yrs), all males]. RESULTS: Analysis of BCR signaling activity in European American and African American PBMC samples revealed that, compared to the European American donors, B cells from African Americans had lower anti-IgD induced phosphorylation of multiple BCR pathway components, including the membrane proximal proteins Syk and SFK as well as proteins in the PI3K pathway (S6 and Akt), the MAPK pathways (Erk and p38), and the NF-κB pathway (NF-κB). In addition to differences in the magnitude of anti-IgD-induced pathway activation, racial differences in BCR signaling kinetic profiles were observed. Further, the frequency of IgD+ B cells differed by race and strongly correlated with BCR pathway activation. Thus, the race-related difference in BCR pathway activation appears to be attributable at least in part to a race-associated difference in IgD+ B cell frequencies. CONCLUSIONS: SCNP analysis enabled the identification of statistically significant race-associated differences in BCR pathway activation within PBMC samples from healthy donors. Understanding race-associated contrasts in immune cell signaling responses may be one critical component for elucidation of differences in immune-mediated disease prevalence and treatment responses.

Elucidating host cell response pathways and repurposing therapeutics for SARS-CoV-2 and other coronaviruses.

COVID-19, first reported in late 2019, is an ongoing pandemic that has been causing devastation across the globe. Although there are multiple vaccines that can prevent severe symptoms, effective COVID-19 therapeutics are still of importance. Using our proprietary in silico engine, we screened more than 22,000 unique compounds represented by over half a million gene expression profiles to uncover compounds that can be repurposed for SARS-CoV-2 and other coronaviruses in a timely and cost-efficient manner. We then tested 13 compounds in vitro and found three with potency against SARS-CoV-2 with reasonable cytotoxicity. Bortezomib and homoharringtonine are some of the most promising hits with IC50 of 1.39 µM and 0.16 µM, respectively for SARS-CoV-2. Tanespimycin and homoharringtonine were effective against the common cold coronaviruses. In-depth analysis highlighted proteasome, ribosome, and heat shock pathways as key targets in modulating host responses during viral infection. Further studies of these pathways and compounds have provided novel and impactful insights into SARS-CoV-2 biology and host responses that could be further leveraged for COVID-19 therapeutics development.

Diminished cytokine-induced Jak/STAT signaling is associated with rheumatoid arthritis and disease activity.

Rheumatoid arthritis (RA) is a systemic and incurable autoimmune disease characterized by chronic inflammation in synovial lining of joints. To identify the signaling pathways involved in RA, its disease activity, and treatment response, we adapted a systems immunology approach to simultaneously quantify 42 signaling nodes in 21 immune cell subsets (e.g., IFNα→p-STAT5 in B cells) in peripheral blood mononuclear cells (PBMC) from 194 patients with longstanding RA (including 98 patients before and after treatment), and 41 healthy controls (HC). We found multiple differences between patients with RA compared to HC, predominantly in cytokine-induced Jak/STAT signaling in many immune cell subsets, suggesting pathways that may be associated with susceptibility to RA. We also found that high RA disease activity, compared to low disease activity, was associated with decreased (e.g., IFNα→p-STAT5, IL-10→p-STAT1) or increased (e.g., IL-6→STAT3) response to stimuli in multiple cell subsets. Finally, we compared signaling in patients with established, refractory RA before and six months after initiation of methotrexate (MTX) or TNF inhibitors (TNFi). We noted significant changes from pre-treatment to post-treatment in IFNα→p-STAT5 signaling and IL-10→p-STAT1 signaling in multiple cell subsets; these changes brought the aberrant RA signaling profiles toward those of HC. This large, comprehensive functional signaling pathway study provides novel insights into the pathogenesis of RA and shows the potential of quantification of cytokine-induced signaling as a biomarker of disease activity or treatment response.

Cell signaling-based classifier predicts response to induction therapy in elderly patients with acute myeloid leukemia.

Single-cell network profiling (SCNP) data generated from multi-parametric flow cytometry analysis of bone marrow (BM) and peripheral blood (PB) samples collected from patients >55 years old with non-M3 AML were used to train and validate a diagnostic classifier (DXSCNP) for predicting response to standard induction chemotherapy (complete response [CR] or CR with incomplete hematologic recovery [CRi] versus resistant disease [RD]). SCNP-evaluable patients from four SWOG AML trials were randomized between Training (N = 74 patients with CR, CRi or RD; BM set = 43; PB set = 57) and Validation Analysis Sets (N = 71; BM set = 42, PB set = 53). Cell survival, differentiation, and apoptosis pathway signaling were used as potential inputs for DXSCNP. Five DXSCNP classifiers were developed on the SWOG Training set and tested for prediction accuracy in an independent BM verification sample set (N = 24) from ECOG AML trials to select the final classifier, which was a significant predictor of CR/CRi (area under the receiver operating characteristic curve AUROC = 0.76, p = 0.01). The selected classifier was then validated in the SWOG BM Validation Set (AUROC = 0.72, p = 0.02). Importantly, a classifier developed using only clinical and molecular inputs from the same sample set (DXCLINICAL2) lacked prediction accuracy: AUROC = 0.61 (p = 0.18) in the BM Verification Set and 0.53 (p = 0.38) in the BM Validation Set. Notably, the DXSCNP classifier was still significant in predicting response in the BM Validation Analysis Set after controlling for DXCLINICAL2 (p = 0.03), showing that DXSCNP provides information that is independent from that provided by currently used prognostic markers. Taken together, these data show that the proteomic classifier may provide prognostic information relevant to treatment planning beyond genetic mutations and traditional prognostic factors in elderly AML.

Inter-donor variation in cell subset specific immune signaling responses in healthy individuals.

Single cell network profiling (SCNP) is a multi-parameter flow cytometry based approach that allows for the simultaneous interrogation of intracellular signaling pathways in multiple cell subpopulations within heterogeneous tissues, without the need for individual cell subset isolation. Thus, the technology is extremely well-suited for characterizing the multitude of interconnected signaling pathways and immune cell subpopulations that regulate the function of the immune system. Recently, SCNP was applied to generate a functional map of the healthy human immune cell signaling network by profiling immune signaling pathways downstream of 12 immunomodulators in 7 distinct immune cell subsets within peripheral blood mononuclear cells (PBMCs) from 60 healthy donors. In the study reported here, the degree of inter-donor variation in the magnitude of the immune signaling responses was analyzed. The highest inter-donor differences in immune signaling pathway activity occurred following perturbation of the immune signaling network, rather than in basal signaling. When examining the full panel of immune signaling responses, as one may expect, the overall degree of inter-donor variation was positively correlated (r = 0.727) with the magnitude of node response (i.e. a larger median signaling response was associated with greater inter-donor variation). However, when examining the degree of heterogeneity across cell subpopulations for individual signaling nodes, cell subset specificity in the degree of inter-donor variation was observed for several nodes. For such nodes, relatively weak correlations between inter-donor variation and the magnitude of the response were observed. Further, within the phenotypically distinct subpopulations, a fraction of the immune signaling responses had bimodal response profiles in which (a) only a portion of the cells had elevated phospho-protein levels following modulation and (b) the proportion of responsive cells varied by donor. These data exemplify the application of SCNP to provide a detailed characterization of inter-donor variation in immune signaling pathway activation in a healthy donor cohort. This dataset provides a basis for identifying cell subpopulation specific immune signaling abnormalities in cancer and immune-mediated diseases. Building upon these data in future studies may help inform on disease etiology, maintenance and therapeutic selection.

Biomediator data integration and inference for functional annotation of anonymous sequences.

Scientists working on genomics projects are often faced with the difficult task of sifting through large amounts of biological information dispersed across various online data sources that are relevant to their area or organism of research. Gene annotation, the process of identifying the functional role of a possible gene, in particular has become increasingly more time-consuming and laborious to conduct as more genomes are sequenced and the number of candidate genes continues to increase at near-exponential pace; genes are left un-annotated, or worse, incorrectly annotated. Many groups have attempted to address the annotation backlog through automated annotation systems that are geared toward specific organisms, and which may thus not possess the necessary flexibility and scalability to annotate other genomes. In this paper, we present a method and framework which attempts to address problems inherent in manual and automatic annotation by coupling a data integration system, BioMediator, to an inference engine with the aim of elucidating functional annotations. The framework and heuristics developed are not specific to any particular genome. We validated the method with a set of randomly-selected annotated sequences from a variety of organisms. Preliminary results show that the hybrid data integration and inference approach generates functional annotations that are as good as or better than "gold standard" annotations approximately 80% of the time.