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BackgroundHigh-throughput screening (HTS) programs are increasingly adopting high-content technologies that can better inform the selection of drug candidates early on in the pipelines. For cancer immunotherapy, proteomics tools to investigate interactions between cancer and immune cells compromise either content or cost, limiting access to phenotypic data. The affordable gold-standard in proteomics, the ELISA, has proven difficult to scale. At fault has been the cross-reactivity between ELISA reagents when multiplexing beyond a few dozen antibody pairs. Here, we describe the nELISA: a massively-parallelized high-throughput miniaturized ELISA with a content, cost and throughput amenable to HTS, and demonstrate its applicability to characterize immune phenotypes in co-culture systems.MethodsTo overcome the long-standing cross-reactivity issue, the nELISA uses DNA oligos to pre-assemble each pair of antibodies onto a spectrally barcoded microparticle set. The resulting reagents are fully-integrated nELISA sensors that can be read-out on commercial cytometers, enabling highly-multiplexed and high-throughput analysis. Using this approach, we developed a comprehensive inflammatory panel containing 191 cytokines, chemokines, proteases, growth factors, and soluble receptors. Our results show that the nELISA can maintain single-plex specificity, sensitivity, and quantification as content is scaled to 191-plex. Furthermore, the nELISA performs at a throughput of 1536 samples/cytometer/day, yielding >300,000 data points in a single day, at a cost amenable to high-throughput screening.ResultsTo demonstrate the nELISA’s utility in HTS, we ran the largest PBMC secretome screen to date, in which >7000 PBMC samples were treated with various inflammatory stimuli, and further perturbed with a selected library of 80 recombinant protein ‘perturbagens’. 191 secreted proteins were profiled in all samples, resulting in ~1.4M datapoints (figure 1A). The nELISA profiles were able to capture phenotypes associated with specific stimulation conditions, individual donors, and potent cytokine perturbagens. By compensating for stimulation and donor differences, we clustered perturbagens according to their effects on PBMC secretomes, identifying well-established cell responses such as Th1 or Th2. Novel phenotypic effects were also identified, such as distinct responses to the near identical CXCL12 alpha and beta isoforms (figure 1B). Interestingly, we observed important similarities between PBMC responses to the cytokine drugs IFN beta and IL-1 Receptor antagonist, supporting the use of anakinra as a replacement for IFN beta in certain indications.ConclusionsThe nELISA captures broad secretome ranges and subtle differences in immune phenotypes, revealing critical insights in cell-based screens. Thus, the nELISA is a powerful new tool for cancer immunotherapy assays, including phenotypic screening, target identification/deconvolution, and discovery of markers of target engagement.Abstract 61 Figure 1High-throughput screen of PBMC responses demonstrates the use of the nELISA for drug discovery. (A) Screen design: PBMCs isolated from six donors were treated with inflammatory stimuli at indicated concentrations, and further perturbed with 80 recombinant cytokine \"perturbagens\", generating a total of 7,392 samples; after 24 hours, concentrations of 191 secreted proteins were measured in the supernatant of each sample using the nELISA. (B) UMAP dimensionality reduction of the entire nELISA dataset; datapoints are colored (from left supernatant of each sample using the nELISA. (B) UMAP dimensionality reduction of the entire nELISA dataset; datapoints are colored (from left to right by stimulation condition, by donor, by stimulation concentration, or by individual cytokind perturbagens with strong effects, as indicated.

Quantitative models of Förster resonance energy transfer (FRET)—pioneered by Förster—define our understanding of FRET and underpin its widespread use. However, multicolour FRET (mFRET), which arises between multiple, stochastically distributed fluorophores, lacks a mechanistic model and remains intractable. mFRET notably arises in fluorescently barcoded microparticles, resulting in a complex, non-orthogonal fluorescence response that impedes their encoding and decoding. Here, we introduce an ensemble mFRET (emFRET) model, and apply it to guide barcoding into regimes with extreme FRET. We further introduce a facile, proportional multicolour labelling method using oligonucleotides as homogeneous linkers. A total of 580 barcodes were rapidly designed and validated using four dyes—with FRET efficiencies reaching 76%—and used for multiplexed immunoassays with cytometric readout and fully automated decoding. The emFRET model helps to expand the barcoding capacity of barcoded microparticles using common organic dyes and will benefit other applications subject to stochastic mFRET.

BackgroundThe identification of novel cancer immunotherapies and the characterization of their immunomodulatory effects is complicated by the plasticity of immune cells and the wide range of phenotypes they may adopt. However, the high cost of capturing this diversity at scale limits typical drug discovery efforts to simple readouts, such as cell killing, or the expression of individual cytokines. We previously described the Nomic platform, a proteomics tool capable of quantifying thousands of proteins at high-throughput and low cost.MethodsHere, we leverage the Nomic platform to screen a library of bioactive small molecules, characterize their immunomodulatory properties, and assess potential toxicities. To achieve this, we collected supernatants from hepatocytes, cardiomyocytes, and microglia treated with 510 compounds at three concentrations. We used Nomic’s Omni 1000 to measure 1,000 proteins across 20,000 samples, generating 20 million data points.ResultsWe identified both expected and novel immune-regulating compounds, and captured in vitro signs of toxicity from therapies that failed clinical development due to dose-limiting toxicities, highlighting the power of our approach. For example, our dataset recapitulated the effects of control compounds such as corticosteroids, which reduced the expression of multiple cytokines while increasing the expression of SAA by hepatocytes, as well as TLR agonists, which dramatically increased the expression of TNFa, IL-12p40, IL-6, and several chemokines. Interestingly, 150 compounds (29%) displayed immunomodulatory properties, many of which were previously unreported. We simultaneously identified potential toxicities of the screened compounds, which was characterized by widespread decreases in protein levels in the supernatant, but increased levels of typically intracellular proteins such as CASP3, GAPDH, IRF3, TYMP, MAPK3, and eIF2a. We identified 68 cytotoxic compounds; of note, while most of these reduced cytokine expression, doxorubicin induced cytokine expression even at non-toxic doses, consistent with its reported pro-inflammatory properties. Other notable exceptions included the GSK-3 inhibitor LY2090314, and the BET inhibitor (+)-JQ1, which were toxic at higher doses, but induced the expression of distinct chemokines at lower doses. Based on the balance of toxicity and immunomodulation, we identified candidates for immunotherapy combinations. For example, the Met inhibitor PF-04217903 potently induced CXCL9-10 and IL-12 expression by hepatocytes, with no signs of toxicity. Considering that PF-04217903 was well tolerated in the clinic but was not pursued for strategic reasons, our results suggest potential for development in combination with checkpoint inhibitors for Met-driven tumors.ConclusionsOur results demonstrate the value of high-throughput proteomics to identify new immunomodulatory compounds and simultaneously characterize their safety profile.

BackgroundImmune phenotypes can be extremely diverse and complex, posing both a challenge to understand them and an opportunity to target them with advanced immune engineering approaches. Unfortunately, proteomics tools that capture the breadth of possible immune responses lack the throughput and affordability to rapidly profile the large sample numbers generated to identify and characterize optimally engineered therapeutic candidates. To overcome this issue, we developed the nELISA: a high-throughput miniaturized ELISA capable of quantifying >275 cytokines, proteases, immune receptors, and growth factors, at 10x-reduced cost compared to previous tools. Here, we applied the nELISA to model systems as a proof-of-concept for use in immune engineering.MethodsWe ran the largest PBMC secretome screen to date, in which ~10,000 PBMC samples were profiled in 1 week, at a throughput of 1536 samples/day. Cells were treated with various inflammatory stimuli, and were further perturbed with a selected library of 80 recombinant protein perturbagens.ResultsThe broad nELISA content enabled us to capture disease phenotypes and donor variability, as well as distinguish between perturbations with similar effects on single markers but vastly different overall phenotypes. For example, IFN gamma was potently induced by >12 perturbagens with very different effects on other cytokines. Thus, IL-23 had almost no impact other than to induce IFN gamma; in contrast, IL-15 also induced IL-1 beta, TNF alpha, CXCL9, CXCL10, and CCL5, whereas IFN beta impacted the expression of >20 cytokines in addition to IFN gamma. Interestingly, we also identified cases perturbagens that could substitute for another, while avoiding deleterious effects. Thus, IL-1 Receptor antagonist shared all of the immunosuppressive effects of IFN beta, without inducing IFN gamma and CXCL10, supporting its use as a replacement for Type I interferons in certain indications such as multiple sclerosis.ConclusionsThese findings highlight the ability of the nELISA to capture a wide range of immune phenotypes at a throughput and cost amenable to immune engineering studies.

BackgroundHigh-plex proteomics is critical to enabling cancer research through comprehensive profiling of immune and tumor-derived signals, facilitating early detection, biomarker discovery, immune-related adverse event (irAE) prediction, and real-time monitoring of therapeutic responses. To date, proteomics has been constrained by flexibility, high costs, and inconsistent quantification. Here, we present Omni 1000, a quantitative 1000-plex solution powered by nELISA technology; designed for broad, reproducible, and cost-effective protein measurement. Omni 1000 delivers 0.1 pg/mL sensitivity, 99.99% specificity, and dynamic range spanning 3-6 logs.MethodsOmni 1000 content was developed through rigorous, data-driven strategy to achieve comprehensive proteome-wide coverage while retaining high-value markers. The foundation is built from two sources: (1) a curated set of Most Valuable Proteins (MVPs)—biomarkers selected heuristically based on prevalence in key signaling pathways, translational research, and validated endpoints; and (2) large-scale, high-plex proteomic datasets with disease-association. To optimize, we iteratively applied Minimum Redundancy Maximum Relevance (mRMR) to reduce overlap, reconstruction loss minimization to preserve signal from high-dimensional datasets, and prioritized MVPs. Each iteration was validated against key biological ontologies—achieving 92% MVP coverage, 100% Reactome level 0, >80% Reactome level 1, 100% pharma-relevant KEGG signaling pathways, and 100% MeSH disease classes. In addition, we determined the disease prediction power of Omni 1000 content at >95% equivalent to a 3000+ panel, evaluated on the UK Biobank cohort.ResultsFor biomarker discovery, Omni 1000 makes large-scale and clinically relevant studies achievable through rapid readout with flow cytometry. We leveraged Omni 1000’s capabilities in a high-thoughput drug screening platform structured on patient-derived tumor organoids. Use of Omni 1000 demonstrated insights on baseline donor heterogeneity and drug compound responses and resistances specific to patient tissue profiles. Of interest for immunotherapy applications were compounds inducing cell death while promoting pro-inflammatory immune environments. We observed cytotoxicity with 2 CDK9 inhibitors in organoids across donors, through increased levels of intracellular proteins in culture supernatant and broad decreases in most other protein levels. They simultaneously resulted in increased secretion of chemokines CXCL2, CXCL3, CXCL5, and maintained CCL2 and IL-8 expression, possibly promoting additional immune involvement parallel to direct cell killing. These findings underscore Omni 1000’s capacity to profile functional heterogeneity in tumor immune microenvironments and support development of precision medicine with immunotherapeutic potential.ConclusionsTogether, we demonstrate a novel 1000-plex solution, Omni 1000, with content balancing critical targets and biological breadth, and demonstrated real-world utility in early detection of disease and high throughput cancer drug development.

BackgroundThe rapid and necessary expansion of cancer drug development requires accessible and scalable strategies for early toxicity screening and immune risk profiling. Liver plays a central role in drug metabolism, detoxification, and immune modulation. Robust detection of liver-specific responses to oncologic therapeutics is essential to identify and mitigate hepatotoxicity and immune-related adverse events (irAEs) which could limit clinical development. Here, we demonstrate a high-throughput, high-plex proteomic platform as a powerful approach to evaluate functional perturbations of hepatocytes across a spectrum of cancer drugs.MethodsUtilizing the novel Omni 1000 proteomic solution, based on nELISA technology, we profiled 1,000 proteins in >6500 samples of primary human hepatocytes following exposure to a diverse panel of over 550 distinct drug compou nds at 3 doses per compound. Well-chosen Omni 1000 protein target content enables simultaneous quantification of stress response markers, inflammatory cytokines, metabolic enzymes, and signaling pathway effectors. This allows for evaluation of both cytotoxic and immunomodulatory signatures, revealing potential off-target effects and mechanistic insights.ResultsPerturbation with tripolide, doxorubicin, and cobimetinib resulted in dose dependent cell toxicity, detected via increased levels of intracellular proteins such as GAPDH and Casp3 released into the supernatant, consistent with clinically observed hepatotoxicity. We also detect increased Galectin-3, Galectin-1, and VEGFA at non-toxic perturbation doses, indicative of cell stress and potential toxicity at higher doses. In addition, we concurrently observe loss of proteins associated with protection against apoptosis, such as ST6GAL1, a potent inhibitor of FAS-mediated apoptosis. Across the 510 compounds screened, 68 showed similar signs of toxicity, including compounds with dose-limiting liver toxicity in the clinic. Of particular note is PHA-793887, a potential therapeutic candidate, which failed clinically due to unexpected hepatotoxicity, which here was captured in a pre-clinical in vitro model.We further demonstrated the platform’s ability to identify likely irAEs through detection of dose dependent increases in key cytokines. Hepatocytes perturbed with Imatinib and Pomalidomide demonstrated increases in IL-6, IL-10, and IL-2, indicative of potential irAE and consistent with published literature for these drugs.ConclusionsHigh-throughput high-plex proteomic analysis of hepatocyte responses offers a significant advance in preclinical toxicology and immune safety, and enables an early stage indication of drug-induced liver injury (DILI) risk and immunotoxic potential, two major barriers in oncology drug development. As the pharmaceutical industry increasingly utilizes systems-level screening, such proteomic platforms will be instrumental in enhancing predictive toxicology, reducing clinical attrition, and accelerating the path from discovery to approval.

BackgroundProteomics holds great promise for cancer immunotherapy, with intensive efforts being exerted for early disease identification, patients selection, and adverse event prediction. Despite this potential, the high cost and low throughput of existing tools to profile circulating proteins render such studies prohibitively slow and costly, limiting their wide-spread application. As a result, proteomics studies in the field have been constrained to sample sizes in the 10s and 100s , restricting the power to discover key biomarkers. Here, we leverage a novel proteomics tool, the nELISA, to quantify ~600 circulating proteins across ~3000 samples from the RADIOHEAD (Resistance Drivers for Immuno-Oncology Patients Interrogated by Harmonized Molecular Datasets) cohort, a prospective study of 1070 immunotherapy naive pan-tumor patients on standard of care immune checkpoint inhibitor (ICI) therapy regimens from community oncology clinics.MethodsThe Nomic platform is a highly multiplexed immunoassay technology that enables the profiling of hundreds of proteins across 1536 samples per instrument daily, at significantly reduced costs. The method miniaturizes sandwich immunoassays by placing antibody pairs on the surface of color-coded microparticles, which can then be analyzed via high-throughput flow cytometry.ResultsUsing this technology, our preliminary data identified several markers of response to treatment; for example, PD-1 inhibitors result in increased circulating levels of soluble PD-1, and ICIs increase levels of several chemokines including CXCL9 and CXCL10, as seen in several other small-scale studies. We also identified several markers potentially predicting response to treatment and irAEs, which will require much larger datasets for validation. The scalable nature of the nELISA platform now allows us to validate these findings in a large longitudinal cohort, providing the power needed for such a broad biomarker discovery effort. In this presentation, we share the results of applying nELISA to the RADIOHEAD blood serum samples from pretreatment, early on-treatment, 6-month, and 12-month timepoints. For participants who experienced immune-related adverse events, additional samples were collected upon presentation and in follow-up visits - these samples were also analyzed in this study.ConclusionsPairing nELISA protein profiling of these longitudinal samples with associated demographic metadata and clinical outcomes provides an opportunity to identify clinically actionable mechanisms for ICI resistance and adverse events, discover targets for combination therapies and post-ICI treatment, and inform system biology approaches to elucidate disease pathways. Here, we highlight biomarkers and protein signatures related to patient outcomes, to reveal additional insights and further accelerate research in the field of cancer immunotherapy.

The immunoassay is a core method for basic and clinical research. Owing to the requirements of dual-antibody binding, the sandwich immunoassay provides exquisite specificity, sensitivity, and cost-efficiency in the measurement of proteins within plasma. Fluorescently-barcoded microparticles are a popular format for immunoassays as they allow multiplexing with high sample throughput and rapid read-out by cytometry. However, this widely used format suffers from two shortcomings that limit its scalability and applicability. First and foremost, the specificity and sensitivity of multiplexed sandwich assays (MSAs) is severely hindered with multiplexing due to cross-reactivity between antibodies applied as a mixture. This reagent-driven cross-reactivity (rCR) continues to be the main obstacle towards increased multiplexing in microparticle-based MSAs. The second challenge is the generation of large numbers of spectrally-distinct microparticles for use in barcoding. Spectral barcoding calls for microparticle functionalization with distinct proportions of multicolour dyes to create a unique barcode; however, multicolour Förster resonance energy transfer (mFRET) leads to hitherto unpredictable shifts in emission intensities thus confounding the barcodes. Thus, current methods for spectral barcoding do not tolerate mFRET, are constrained to using specialized dyes and cytometers that minimize the spectral overlap, and employ lengthy trial-and-error optimization protocols to experimentally determine the barcode-specific dye ratios.Here, we introduce a platform for (i) rCR-free multiplexing of sandwich assays on microparticles with (ii) mFRET-tolerating high-capacity barcoding. Building on the work of Förster, we introduce an ensemble multicolour FRET (emFRET) model that can accurately predict the ensemble spectral profiles of any combination of dyes. We also developed a facile, proportional microparticle labelling method to achieve proportional labeling, conjugating the dyes to DNA oligonucleotides, which served as chemically-homogeneous cross-linkers. The emFRET model enabled in silico design of 580 distinguishable barcodes using dyes with strong spectral overlap (FAM, Cy3, Cy5, Cy5.5), and with inter-dye FRET efficiency reaching up to 0.76. The emFRET also allowed robust and fully-automated decoding without need for manual calibration. Taken together, the results established a platform for rapid, high-capacity microparticle barcoding using common dyes, and fully automated decoding using commonly used cytometers, and which can be used for immunoassays.linkage assay on microparticles\" (CLAMP). In our approach both members of an antibody pair are pre-immobilized to the surface of micron-sized microparticles, with one of the members tethered via a cleavable DNA linker. Different microparticle sets can be prepared, each containing both affinity reagents for specific detection of their target analyte. Importantly, only complete protein-antibody sandwich complexes on each bead are labeled using a DNA-displacement strategy, prior to rapid read-out of CLAMPs by flow cytometry. The CLAMP assay was optimized by tuning the valency and surface density of antibody conjugates, and was shown to eliminate rCR from microparticlebased MSAs. The CLAMP assay holds potential as the first scalable MSA on microparticles, and paves the way for simple, rapid, and high-throughput MSAs. By pre-assembling antibody pairs, this immunoassay concept represents a departure from typical immunoassays, and offers the possibility to design the multivalent sensors a priori, which could enable higher sensitivity assays.In this dissertation, we have presented an integrated platform that addresses two of the most important and highly sought-for scaling challenges for MSAs. These results set the foundation for the next-generation of protein assays that can be multiplexed while maintaining the excellent sensitivity and specificity of the single-plex ELISAs. Importantly, the approach taken in this work is cost-efficient and can be immediately deployed, enabling application in large population-wide studies and promising to meet the increasing demands in precision medicine.

The fact that wars are pervasive throughout Israel's biblical history is not surprising since it is in harmony with the socio-political and economic milieu of the ancient Near East. Sacral wars are wars that Yahweh sanctions or wars in which he directly intervenes to defeat his enemies. Yahweh's involvement in Israel's wars may be somewhat surprising to a casual reader of the Old Testament, but students of ancient cultures have long established that such a phenomenon is not unique to biblical history. Gods of ancient Near Eastern nations were just as involved in the wars of their people. The problem that these texts raise for biblical theology lies in what appears to the modern day readers to be extreme and unnecessary violence. The total annihilation (i.e., hērem) of entire Canaanite communities in the conquest wars is a prime example. This study seeks to understand the relationship of sacral war ideology to Old Testament theology, dealing with the text in its final canonical form and taking seriously both its historicity and chronology. It takes into account some insights of literary and tradition criticism in as much as they affect the exegesis and theology of the biblical text. It approaches the topic by isolating six different but related biblical motifs (Israel's election, deliverance from Egypt, the land promise, judgment, religious purity, and covenantal curses) that form the theological foundations of sacral war in ancient Israel. Then it traces all six motifs in the prophetic literature as they relate to Israel's historical and eschatological future. The thesis of this study is twofold. First, irrespective of its outcome (victory or defeat) sacral war in Israers history is covenantal. Israel's covenantal relationship to Yahweh provided the occasion (election), limitation (land promise), and historical manifestation (exodus deliverance and conquest) of sacral war. Her participation in sacral war against the Canaanites (whether to exact judgment or to protect her religious purity) was out of covenantal obligation rather than ethnic discrimination. Second, even though sacral war survived in the prophetic visions of the future, it has undergone a significant inversion (a new exodus that will eclipse the first, inclusion of the nations, and universalization of Yahwistic theocracy to encompass the \"whole earth\"). The changes are related to the \"new covenant\" which will bring everlasting peace. The prophets, by entertaining the hope of abolishing death, added a seventh motif that opened the door to the concept of martyrdom.

We present the nELISA, a high-throughput, high-fidelity, and high-plex protein profiling platform. DNA oligonucleotides are used to pre-assemble antibody pairs on spectrally encoded microparticles and perform displacement-mediated detection. Spatial separation between non-cognate antibodies prevents the rise of reagent-driven cross-reactivity, while read-out is performed cost-efficiently and at high-throughput using flow cytometry. nELISA can measure both protein concentration and their post-translational modifications. We assembled an inflammatory panel of 191 targets that were multiplexed without cross-reactivity nor impact on performance vs 1-plex signals, with sensitivities as low as 0.1 pg/mL and measurements spanning 7 orders of magnitude. We then performed a large-scale inflammatory-secretome perturbation screen of peripheral blood mononuclear cells (PBMCs), with cytokines as both perturbagens and readouts, measuring 7,392 samples and generating ~1.4M protein data points in under a week; a significant advance in throughput compared to other highly multiplexed immunoassays. We uncovered 447 significant cytokine responses, including multiple putatively novel ones, that were conserved across donors and stimulation conditions. We validate nELISA for phenotypic screening, where its capacity to faithfully report hundreds of proteins make it a powerful tool across multiple stages of drug discovery.