Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma.

To define the cellular composition and architecture of cutaneous squamous cell carcinoma (cSCC), we combined single-cell RNA sequencing with spatial transcriptomics and multiplexed ion beam imaging from a series of human cSCCs and matched normal skin. cSCC exhibited four tumor subpopulations, three recapitulating normal epidermal states, and a tumor-specific keratinocyte (TSK) population unique to cancer, which localized to a fibrovascular niche. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing TSK cells as a hub for intercellular communication. Multiple features of potential immunosuppression were observed, including T regulatory cell (Treg) co-localization with CD8 T cells in compartmentalized tumor stroma. Finally, single-cell characterization of human tumor xenografts and in vivo CRISPR screens identified essential roles for specific tumor subpopulation-enriched gene networks in tumorigenesis. These data define cSCC tumor and stromal cell subpopulations, the spatial niches where they interact, and the communicating gene networks that they engage in cancer.

Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics.

Single-cell RNA sequencing (scRNA-seq) identifies cell subpopulations within tissue but does not capture their spatial distribution nor reveal local networks of intercellular communication acting in situ. A suite of recently developed techniques that localize RNA within tissue, including multiplexed in situ hybridization and in situ sequencing (here defined as high-plex RNA imaging) and spatial barcoding, can help address this issue. However, no method currently provides as complete a scope of the transcriptome as does scRNA-seq, underscoring the need for approaches to integrate single-cell and spatial data. Here, we review efforts to integrate scRNA-seq with spatial transcriptomics, including emerging integrative computational methods, and propose ways to effectively combine current methodologies.

PROBER identifies proteins associated with programmable sequence-specific DNA in living cells.

DNA-protein interactions mediate physiologic gene regulation and may be altered by DNA variants linked to polygenic disease. To enhance the speed and signal-to-noise ratio (SNR) in the identification and quantification of proteins associated with specific DNA sequences in living cells, we developed proximal biotinylation by episomal recruitment (PROBER). PROBER uses high-copy episomes to amplify SNR, and proximity proteomics (BioID) to identify the transcription factors and additional gene regulators associated with short DNA sequences of interest. PROBER quantified both constitutive and inducible association of transcription factors and corresponding chromatin regulators to target DNA sequences and binding quantitative trait loci due to single-nucleotide variants. PROBER identified alterations in regulator associations due to cancer hotspot mutations in the hTERT promoter, indicating that these mutations increase promoter association with specific gene activators. PROBER provides an approach to rapidly identify proteins associated with specific DNA sequences and their variants in living cells.

Challenges and opportunities in network-based solutions for biological questions.

Network biology is useful for modeling complex biological phenomena; it has attracted attention with the advent of novel graph-based machine learning methods. However, biological applications of network methods often suffer from inadequate follow-up. In this perspective, we discuss obstacles for contemporary network approaches-particularly focusing on challenges representing biological concepts, applying machine learning methods, and interpreting and validating computational findings about biology-in an effort to catalyze actionable biological discovery.

The proximal proteome of 17 SARS-CoV-2 proteins links to disrupted antiviral signaling and host translation.

Viral proteins localize within subcellular compartments to subvert host machinery and promote pathogenesis. To study SARS-CoV-2 biology, we generated an atlas of 2422 human proteins vicinal to 17 SARS-CoV-2 viral proteins using proximity proteomics. This identified viral proteins at specific intracellular locations, such as association of accessary proteins with intracellular membranes, and projected SARS-CoV-2 impacts on innate immune signaling, ER-Golgi transport, and protein translation. It identified viral protein adjacency to specific host proteins whose regulatory variants are linked to COVID-19 severity, including the TRIM4 interferon signaling regulator which was found proximal to the SARS-CoV-2 M protein. Viral NSP1 protein adjacency to the EIF3 complex was associated with inhibited host protein translation whereas ORF6 localization with MAVS was associated with inhibited RIG-I 2CARD-mediated IFNB1 promoter activation. Quantitative proteomics identified candidate host targets for the NSP5 protease, with specific functional cleavage sequences in host proteins CWC22 and FANCD2. This data resource identifies host factors proximal to viral proteins in living human cells and nominates pathogenic mechanisms employed by SARS-CoV-2.

Single-Cell and Spatial Transcriptomic Analysis of Human Skin Delineates Intercellular Communication and Pathogenic Cells.

Epidermal homeostasis is governed by a balance between keratinocyte proliferation and differentiation with contributions from cell-cell interactions, but conserved or divergent mechanisms governing this equilibrium across species and how an imbalance contributes to skin disease are largely undefined. To address these questions, human skin single-cell RNA sequencing and spatial transcriptomics data were integrated and compared with mouse skin data. Human skin cell-type annotation was improved using matched spatial transcriptomics data, highlighting the importance of spatial context in cell-type identity, and spatial transcriptomics refined cellular communication inference. In cross-species analyses, we identified a human spinous keratinocyte subpopulation that exhibited proliferative capacity and a heavy metal processing signature, which was absent in mouse and may account for species differences in epidermal thickness. This human subpopulation was expanded in psoriasis and zinc-deficiency dermatitis, attesting to disease relevance and suggesting a paradigm of subpopulation dysfunction as a hallmark of the disease. To assess additional potential subpopulation drivers of skin diseases, we performed cell-of-origin enrichment analysis within genodermatoses, nominating pathogenic cell subpopulations and their communication pathways, which highlighted multiple potential therapeutic targets. This integrated dataset is encompassed in a publicly available web resource to aid mechanistic and translational studies of normal and diseased skin.

Integrative analyses highlight functional regulatory variants associated with neuropsychiatric diseases.

Noncoding variants of presumed regulatory function contribute to the heritability of neuropsychiatric disease. A total of 2,221 noncoding variants connected to risk for ten neuropsychiatric disorders, including autism spectrum disorder, attention deficit hyperactivity disorder, bipolar disorder, borderline personality disorder, major depression, generalized anxiety disorder, panic disorder, post-traumatic stress disorder, obsessive-compulsive disorder and schizophrenia, were studied in developing human neural cells. Integrating epigenomic and transcriptomic data with massively parallel reporter assays identified differentially-active single-nucleotide variants (daSNVs) in specific neural cell types. Expression-gene mapping, network analyses and chromatin looping nominated candidate disease-relevant target genes modulated by these daSNVs. Follow-up integration of daSNV gene editing with clinical cohort analyses suggested that magnesium transport dysfunction may increase neuropsychiatric disease risk and indicated that common genetic pathomechanisms may mediate specific symptoms that are shared across multiple neuropsychiatric diseases.

A cis-regulatory lexicon of DNA motif combinations mediating cell-type-specific gene regulation.

Gene expression is controlled by transcription factors (TFs) that bind cognate DNA motif sequences in cis-regulatory elements (CREs). The combinations of DNA motifs acting within homeostasis and disease, however, are unclear. Gene expression, chromatin accessibility, TF footprinting, and H3K27ac-dependent DNA looping data were generated and a random-forest-based model was applied to identify 7,531 cell-type-specific cis-regulatory modules (CRMs) across 15 diploid human cell types. A co-enrichment framework within CRMs nominated 838 cell-type-specific, recurrent heterotypic DNA motif combinations (DMCs), which were functionally validated using massively parallel reporter assays. Cancer cells engaged DMCs linked to neoplasia-enabling processes operative in normal cells while also activating new DMCs only seen in the neoplastic state. This integrative approach identifies cell-type-specific cis-regulatory combinatorial DNA motifs in diverse normal and diseased human cells and represents a general framework for deciphering cis-regulatory sequence logic in gene regulation.

The proximal proteome of 17 SARS-CoV-2 proteins links to disrupted antiviral signaling and host translation.

Viral proteins localize within subcellular compartments to subvert host machinery and promote pathogenesis. To study SARS-CoV-2 biology, we generated an atlas of 2422 human proteins vicinal to 17 SARS-CoV-2 viral proteins using proximity proteomics. This identified viral proteins at specific intracellular locations, such as association of accessary proteins with intracellular membranes, and projected SARS-CoV-2 impacts on innate immune signaling, ER-Golgi transport, and protein translation. It identified viral protein adjacency to specific host proteins whose regulatory variants are linked to COVID-19 severity, including the TRIM4 interferon signaling regulator which was found proximal to the SARS-CoV-2 M protein. Viral NSP1 protein adjacency to the EIF3 complex was associated with inhibited host protein translation whereas ORF6 localization with MAVS was associated with inhibited RIG-I 2CARD-mediated IFNB1 promoter activation. Quantitative proteomics identified candidate host targets for the NSP5 protease, with specific functional cleavage sequences in host proteins CWC22 and FANCD2. This data resource identifies host factors proximal to viral proteins in living human cells and nominates pathogenic mechanisms employed by SARS-CoV-2. AUTHOR SUMMARY: SARS-CoV-2 is the latest pathogenic coronavirus to emerge as a public health threat. We create a database of proximal host proteins to 17 SARS-CoV-2 viral proteins. We validate that NSP1 is proximal to the EIF3 translation initiation complex and is a potent inhibitor of translation. We also identify ORF6 antagonism of RNA-mediate innate immune signaling. We produce a database of potential host targets of the viral protease NSP5, and create a fluorescence-based assay to screen cleavage of peptide sequences. We believe that this data will be useful for identifying roles for many of the uncharacterized SARS-CoV-2 proteins and provide insights into the pathogenicity of new or emerging coronaviruses.

Pathway and network embedding methods for prioritizing psychiatric drugs.

One in five Americans experience mental illness, and roughly 75% of psychiatric prescriptions do not successfully treat the patient's condition. Extensive evidence implicates genetic factors and signaling disruption in the pathophysiology of these diseases. Changes in transcription often underlie this molecular pathway dysregulation; individual patient transcriptional data can improve the efficacy of diagnosis and treatment. Recent large-scale genomic studies have uncovered shared genetic modules across multiple psychiatric disorders - providing an opportunity for an integrated multi-disease approach for diagnosis. Moreover, network-based models informed by gene expression can represent pathological biological mechanisms and suggest new genes for diagnosis and treatment. Here, we use patient gene expression data from multiple studies to classify psychiatric diseases, integrate knowledge from expert-curated databases and publicly available experimental data to create augmented disease-specific gene sets, and use these to recommend disease-relevant drugs. From Gene Expression Omnibus, we extract expression data from 145 cases of schizophrenia, 82 cases of bipolar disorder, 190 cases of major depressive disorder, and 307 shared controls. We use pathway-based approaches to predict psychiatric disease diagnosis with a random forest model (78% accuracy) and derive important features to augment available drug and disease signatures. Using protein-protein-interaction networks and embedding-based methods, we build a pipeline to prioritize treatments for psychiatric diseases that achieves a 3.4-fold improvement over a background model. Thus, we demonstrate that gene-expression-derived pathway features can diagnose psychiatric diseases and that molecular insights derived from this classification task can inform treatment prioritization for psychiatric diseases.

A Literature-Based Knowledge Graph Embedding Method for Identifying Drug Repurposing Opportunities in Rare Diseases.

Millions of Americans are affected by rare diseases, many of which have poor survival rates. However, the small market size of individual rare diseases, combined with the time and capital requirements of pharmaceutical R&D, have hindered the development of new drugs for these cases. A promising alternative is drug repurposing, whereby existing FDA-approved drugs might be used to treat diseases different from their original indications. In order to generate drug repurposing hypotheses in a systematic and comprehensive fashion, it is essential to integrate information from across the literature of pharmacology, genetics, and pathology. To this end, we leverage a newly developed knowledge graph, the Global Network of Biomedical Relationships (GNBR). GNBR is a large, heterogeneous knowledge graph comprising drug, disease, and gene (or protein) entities linked by a small set of semantic themes derived from the abstracts of biomedical literature. We apply a knowledge graph embedding method that explicitly models the uncertainty associated with literature-derived relationships and uses link prediction to generate drug repurposing hypotheses. This approach achieves high performance on a gold-standard test set of known drug indications (AUROC = 0.89) and is capable of generating novel repurposing hypotheses, which we independently validate using external literature sources and protein interaction networks. Finally, we demonstrate the ability of our model to produce explanations of its predictions.

Automated Detection of Diabetic Retinopathy using Deep Learning.

Diabetic retinopathy is a leading cause of blindness among working-age adults. Early detection of this condition is critical for good prognosis. In this paper, we demonstrate the use of convolutional neural networks (CNNs) on color fundus images for the recognition task of diabetic retinopathy staging. Our network models achieved test metric performance comparable to baseline literature results, with validation sensitivity of 95%. We additionally explored multinomial classification models, and demonstrate that errors primarily occur in the misclassification of mild disease as normal due to the CNNs inability to detect subtle disease features. We discovered that preprocessing with contrast limited adaptive histogram equalization and ensuring dataset fidelity by expert verification of class labels improves recognition of subtle features. Transfer learning on pretrained GoogLeNet and AlexNet models from ImageNet improved peak test set accuracies to 74.5%, 68.8%, and 57.2% on 2-ary, 3-ary, and 4-ary classification models, respectively.

Development and initial validation of a novel smoothed-particle hydrodynamics-based simulation model of trabecular bone penetration by metallic implants.

A novel computational model of implant migration in trabecular bone was developed using smoothed-particle hydrodynamics (SPH), and an initial validation was performed via correlation with experimental data. Six fresh-frozen human cadaveric specimens measuring 10 × 10 × 20 mm were extracted from the proximal femurs of female donors (mean age of 82 years, range 75-90, BV/TV ratios between 17.88% and 30.49%). These specimens were then penetrated under axial loading to a depth of 10 mm with 5 mm diameter cylindrical indenters bearing either flat or sharp/conical tip designs similar to blunt and self-tapping cancellous screws, assigned in a random manner. SPH models were constructed based on microCT scans (17.33 µm) of the cadaveric specimens. Two initial specimens were used for calibration of material model parameters. The remaining four specimens were then simulated in silico using identical material model parameters. Peak forces varied between 92.0 and 365.0 N in the experiments, and 115.5-352.2 N in the SPH simulations. The concordance correlation coefficient between experimental and simulated pairs was 0.888, with a 95%CI of 0.8832-0.8926, a Pearson ρ (precision) value of 0.9396, and a bias correction factor Cb (accuracy) value of 0.945. Patterns of bone compaction were qualitatively similar; both experimental and simulated flat-tipped indenters produced dense regions of compacted material adjacent to the advancing face of the indenter, while sharp-tipped indenters deposited compacted material along their peripheries. Simulations based on SPH can produce accurate predictions of trabecular bone penetration that are useful for characterizing implant performance under high-strain loading conditions. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:1114-1123, 2018.

A novel fracture mechanics model explaining the axial penetration of bone-like porous, compressible solids by various orthopaedic implant tips.

Many features of orthopaedic implants have been previously examined regarding their influence on migration in trabecular bone under axial loading, with screw thread design being one of the most prominent examples. There has been comparatively little investigation, however, of the influence that implant tip design has on migration under axial loads. We present a novel fracture mechanics model that explains how differences in tip design affect the force required for axial penetration of porous, compressible solids similar to trabecular bone. Three tip designs were considered based on typical 5 mm diameter orthopaedic locking screws: flat and conical tip designs, as well as a novel elastomeric tip. Ten axial penetration trials were conducted for each tip design. In order to isolate the effect of tip design on axial migration from that of the threads, smooth steel rods were used. Tip designs were inserted into polyurethane foam commonly used to represent osteoporotic trabecular bone tissue (ASTM Type 10, 0.16 g/cc) to a depth of 10 mm at a rate of 2 mm/min, while force and position were recorded. At maximum depth, elastomeric tips were found to require the greatest force for axial migration (mean of 248.24 N, 95% Confidence Interval [CI]: 238.1-258.4 N), followed by conical tips (mean of 143.46 N, 95% CI: 142.1-144.9 N), and flat tips (mean of 113.88 N, 95% CI: 112.2-115.5 N). This experiment was repeated in cross-section while recording video of material compaction through a transparent window. Strain fields for each tip design were then generated from these videos using digital image correlation (DIC) software. A novel fracture mechanics model, combining the Griffith with porous material compaction, was developed to explain the performance differences observed between the three tip designs. This model predicted that steady-state stress would be roughly the same (~ 4 MPa) across all designs, a finding consistent with the experimental results. The model also suggested that crack formation and friction are negligible mechanisms of energy absorption during axial penetration of porous compressible solids similar to trabecular bone. Material compaction appears to be the dominant mechanism of energy absorption, regardless of tip design. The cross-sectional area of the compacted material formed during migration of the implant tip during axial penetration was shown to be a strong determinant of the force required for migration to occur (Pearson Coefficient = 0.902, p < .001). As such, implant tips designed to maximize the cross-sectional area of compacted material - such as the elastomeric and conical tips in the present study - may be useful in reducing excessive implant migration under axial loads in trabecular bone.

Local remodeling of synthetic extracellular matrix microenvironments by co-cultured endometrial epithelial and stromal cells enables long-term dynamic physiological function.

Mucosal barrier tissues, comprising a layer of tightly-bonded epithelial cells in intimate molecular communication with an underlying matrix-rich stroma containing fibroblasts and immune cells, are prominent targets for drugs against infection, chronic inflammation, and other disease processes. Although human in vitro models of such barriers are needed for mechanistic studies and drug development, differences in extracellular matrix (ECM) needs of epithelial and stromal cells hinder efforts to create such models. Here, using the endometrium as an example mucosal barrier, we describe a synthetic, modular ECM hydrogel suitable for 3D functional co-culture, featuring components that can be remodeled by cells and that respond dynamically to sequester local cell-secreted ECM characteristic of each cell type. The synthetic hydrogel combines peptides with off-the-shelf reagents and is thus accessible to cell biology labs. Specifically, we first identified a single peptide as suitable for initial attachment of both endometrial epithelial and stromal cells using a 2D semi-empirical screen. Then, using a co-culture system of epithelial cells cultured on top of gel-encapsulated stromal cells, we show that inclusion of ECM-binding peptides in the hydrogel, along with the integrin-binding peptide, leads to enhanced accumulation of basement membrane beneath the epithelial layer and more fibrillar collagen matrix assembly by stromal cells over two weeks in culture. Importantly, endometrial co-cultures composed of either cell lines or primary cells displayed hormone-mediated differentiation as assessed by morphological changes and secretory protein production. A multiplex analysis of apical cytokine and growth factor secretion comparing cell lines and primary cells revealed strikingly different patterns, underscoring the importance of using primary cell models in analysis of cell-cell communication networks. In summary, we define a "one-size-fits-all" synthetic ECM that enables long-term, physiologically responsive co-cultures of epithelial and stromal cells in a mucosal barrier format.

Incorporating an Early Detection System Into Routine Clinical Practice in Two Community Hospitals.

Efforts to improve outcomes of patients who deteriorate outside the intensive care unit have included the use of rapid response teams (RRTs) as well as manual and automated prognostic scores. Although automated early warning systems (EWSs) are starting to enter clinical practice, there are few reports describing implementation and the processes required to integrate early warning approaches into hospitalists' workflows. We describe the implementation process at 2 community hospitals that deployed an EWS. We employed the Institute for Healthcare Improvement's iterative Plan-Do-Study-Act approach. Our basic workflow, which relies on having an RRT nurse and the EWS's 12-hour outcome time frame, has been accepted by clinicians and has not been associated with patient complaints. Whereas our main objective was to develop a set of workflows for integrating the electronic medical record EWS into clinical practice, we also uncovered issues that must be addressed prior to disseminating this intervention to other hospitals. One problematic area is that of documentation following an alert. Other areas that must be addressed prior to disseminating the intervention include the need for educating clinicians on the rationale for deploying the EWS, careful consideration of interdepartment service agreements, clear definition of clinician responsibilities, pragmatic documentation standards, and how to communicate with patients. In addition to the deployment of the EWS to other hospitals, a future direction for our teams will be to characterize process-outcomes relationships in the clinical response itself. Journal of Hospital Medicine 2016;11:S25-S31. © 2016 Society of Hospital Medicine.

Clustering of capnogram features to track state transitions during procedural sedation.

Procedural sedation has allowed many painful interventions to be conducted outside the operating room. During such procedures, it is important to maintain an appropriate level of sedation to minimize the risk of respiratory depression if patients are over-sedated and added pain or anxiety if under-sedated. However, there is currently no objective way to measure the patient's evolving level of sedation during a procedure. We investigated the use of capnography-derived features as an objective measure of sedation level. Time-based capnograms were recorded from 30 patients during sedation for cardioversion. Through causal k-means clustering of selected features, we sequentially assigned each exhalation to one of three distinct clusters, or states. Transitions between these states correlated to events during sedation (drug administration, procedure start and end, and clinical interventions). Similar clustering of capnogram recordings from 26 healthy, non-sedated subjects did not reveal distinctly separated states.