Post-acute sequelae of COVID-19 (PASC) represent an emerging global crisis. However, quantifiable risk-factors for PASC and their biological associations are poorly resolved. We executed a deep multi-omic, longitudinal investigation of 309 COVID-19 patients from initial diagnosis to convalescence (2-3 months later), integrated with clinical data, and patient-reported symptoms. We resolved four PASC-anticipating risk factors at the time of initial COVID-19 diagnosis: type 2 diabetes, SARS-CoV-2 RNAemia, Epstein-Barr virus viremia, and specific autoantibodies. In patients with gastrointestinal PASC, SARS-CoV-2-specific and CMV-specific CD8 + T cells exhibited unique dynamics during recovery from COVID-19. Analysis of symptom-associated immunological signatures revealed coordinated immunity polarization into four endotypes exhibiting divergent acute severity and PASC. We find that immunological associations between PASC factors diminish over time leading to distinct convalescent immune states. Detectability of most PASC factors at COVID-19 diagnosis emphasizes the importance of early disease measurements for understanding emergent chronic conditions and suggests PASC treatment strategies.

We present an integrated analysis of the clinical measurements, immune cells and plasma multi-omics of 139 COVID-19 patients representing all levels of disease severity, from serial blood draws collected during the first week of infection following diagnosis. We identify a major shift between mild and moderate disease, at which point elevated inflammatory signaling is accompanied by the loss of specific classes of metabolites and metabolic processes. Within this stressed plasma environment at moderate disease, multiple unusual immune cell phenotypes emerge and amplify with increasing disease severity. We condensed over 120,000 immune features into a single axis to capture how different immune cell classes coordinate in response to SARS-CoV-2. This immune-response axis independently aligns with the major plasma composition changes, with clinical metrics of blood clotting, and with the sharp transition between mild and moderate disease. This study suggests that moderate disease may provide the most effective setting for therapeutic intervention.

We collected personal, dense, dynamic data for 108 individuals over 9 months, including whole genome sequence; clinical tests, metabolomes, proteomes and microbiomes at three time points; and daily activity tracking. Using these data we generated a correlation network and identified communities of related analytes that were associated with physiology and disease. We demonstrate how connectivity within these communities identified known and candidate biomarkers, e.g. gamma-glutamyltyrosine was densely interconnected with clinical analytes for cardiometabolic disease. We calculated polygenic scores from GWAS for 127 traits and diseases, and identified molecular correlates of polygenic risk, e.g. genetic risk for inflammatory bowel disease was negatively correlated with plasma cystine. Finally, behavioral coaching informed by personalized data helped participants improve clinical biomarkers. Personal, dense, dynamic data clouds will improve understanding of health and disease, especially for early transition states. This approach to “scientific wellness” represents an opportunity largely missing in contemporary health care.

Exosomes are endosome-derived membrane vesicles carrying proteins and nucleic acids that are involved in cellular functions such as intercellular communication, protein and RNA secretion, and antigen presentation. Therefore, exosomes serve as potential biomarkers for many diseases including cancer. Because exosomes are difficult to enrich or purify from biofluids, quantification of exosomes is tedious and inaccurate. Here, we present a real-time, label-free, and quantitative method to detect and characterize tumor-derived exosomes without enrichment or purification. Utilizing surface plasmon resonance imaging (SPRi) in combination with antibody microarrays specific to the extracellular domains of exosome membrane proteins, exosomes in tumor cell culture medium can be quantitatively detected. We found a positive correlation between the metastatic potential of tumor cell lines and exosome secretion. This method provides an easy, efficient, and novel way to detect exosome secretion and thus an avenue toward the diagnosis and prognosis prediction of cancer.

Surface plasmon resonance imaging (SPRi) is a label-free technique used for the quantitation of binding affinities and concentrations for a wide variety of target molecules. Although SPRi is capable of determining binding constants for multiple ligands in parallel, current commercial instruments are limited to a single analyte stream on multiple ligand spots. Measurement of binding kinetics requires the serial introduction of different analyte concentrations; such repeated experiments are conducted manually and are therefore time-intensive. To address these challenges, we have developed an integrated microfluidic array using soft lithography techniques for high-throughput SPRi-based detection and determination of binding affinities of antibodies against protein targets. The device consists of 264 element-addressable chambers isolated by microvalves. The resulting 700 pL chamber volumes, combined with a serial dilution network for simultaneous interrogation of up to six different analyte concentrations, allow for further speeding detection times. To test for device performance, human alpha-thrombin was immobilized on the sensor surface and anti-human alpha-thrombin IgG was injected across the surface at different concentrations. The equilibrium dissociation constant was determined to be 5.0 +/- 1.9 nM, which agrees well with values reported in the literature. The interrogation of multiple ligands to multiple analytes in a single device was also investigated and samples were recovered with no cross-contamination. Since each chamber can be addressed independently, this array is capable of interrogating binding events from up to 264 different immobilized ligands against multiple analytes in a single experiment. The development of high-throughput protein analytic measurements is a critical technology for systems approaches to biology and medicine.

Background: The tumor microenvironment (TME) is critical to every aspect of cancer biology.Organotypic tumor slice cultures (TSCs) preserve the original TME and have demonstrated utility in predicting drug sensitivity, but the association between clinicopathologic parameters and in vitro TSC behavior has not been well-defined.Methods: One hundred and eight fresh tumor specimens from liver resections at a tertiary academic center were procured and precisely cut with a Vibratome to create 250 μm × 6 mm slices. These fixed-dimension TSCs were grown on polytetrafluoroethylene inserts, and their metabolic activities were determined by a colorimetric assay. Correlation between baseline activities and clinicopathologic parameters was assessed. Tissue CEA mRNA expression was determined by RNAseq.Results: By standardizing the dimensions of a slice, we found that adjacent tumor slices have equivalent metabolic activities, while those derived from different tumors exhibit >30-fold range in baseline MTS absorbances, which correlated significantly with the percentage of tumor necrosis based on histologic assessment. Extending this to individual cancers, we were able to detect intra-tumoral heterogeneity over a span of a few millimeters, which reflects differences in tumor cell density and Ki-67 positivity. For colorectal cancers, tissue CEA expression based on RNAseq of tumor slices was found to correlate with clinical response to chemotherapies. Conclusions:We report a standardized method to assess and compare human cancer growth ex vivo across a wide spectrum of tumor samples. TSC reflects the state of tumor behavior and heterogeneity, thus providing a simple approach to study of human cancers with an intact TME.

The detection and quantification of specific proteins in complex mixtures is a major challenge for proteomics. For example, the development of disease-related biomarker panels will require fast and efficient methods for obtaining multiparameter protein profiles. We established a high throughput, label-free method for analyzing serum using surface plasmon resonance imaging of antibody microarrays. Microarrays were fabricated using standard pin spotting on bare gold substrates, and samples were applied for binding analysis using a camera-based surface plasmon resonance system. We validated the system by measuring the concentrations of four serum proteins using part of a 792-feature microarray. Transferrin concentrations were measured to be 2.1 mg/ml in human serum and 1.2 mg/ml in murine serum, which closely matched ELISA determinations of 2.6 and 1.2 mg/ml, respectively. In agreement with expected values, human and mouse albumin levels were measured to be 24.3 and 23.6 mg/ml, respectively. The lower limits of detection for the four measurements ranged from 14 to 58 ng/ml or 175 to 755 pM. Where purified target proteins are not available for calibration, the microarrays can be used for relative protein quantification. We used the antibody microarray to compare the serum protein profiles from three liver cancer patients and three non-liver cancer patients. Hierarchical clustering of the serum protein levels clearly distinguished two distinct profiles. Thirty-nine significant protein changes were detected (p < 0.05), 10 of which have been observed previously in serum. ␣-Fetoprotein, a known liver cancer marker, was observed to increase. These results demonstrate the feasibility of this high throughput approach for both absolute and relative protein expression profiling. Molecular & Cellular Proteomics 7:2464 -2474, 2008.

We discuss here a new approach to detecting hepatotoxicity by employing concentration changes of liver-specific blood proteins during disease progression. These proteins are capable of assessing the behaviors of their cognate liver biological networks for toxicity or disease perturbations. Blood biomarkers are highly desirable diagnostics as blood is easily accessible and baths virtually all organs. Fifteen liver-specific blood proteins were identified as markers of acetaminophen (APAP)-induced hepatotoxicity using three proteomic technologies: label-free antibody microarrays, quantitative immunoblotting, and targeted iTRAQ mass spectrometry. Liver-specific blood proteins produced a toxicity signature of eleven elevated and four attenuated blood protein levels. These blood protein perturbations begin to provide a systems view of key mechanistic features of APAP-induced liver injury relating to glutathione and S-adenosyl-L-methionine (SAMe) depletion, mitochondrial dysfunction, and liver responses to the stress. Two markers, elevated membrane-bound catechol-O-methyltransferase (MB-COMT) and attenuated retinol binding protein 4 (RBP4), report hepatic injury significantly earlier than the current gold standard liver biomarker, alanine transaminase (ALT). These biomarkers were perturbed prior to onset of irreversible liver injury. Ideal markers should be applicable for both rodent model studies and human clinical trials. Five of these mouse liver-specific blood markers had human orthologs that were also found to be responsive to human hepatotoxicity. This panel of liver-specific proteins has the potential to effectively identify the early toxicity onset, the nature and extent of liver injury and report on some of the APAP-perturbed liver networks.