Quantifying the clusterness and trajectoriness of single-cell RNA-seq data.

Among existing computational algorithms for single-cell RNA-seq analysis, clustering and trajectory inference are two major types of analysis that are routinely applied. For a given dataset, clustering and trajectory inference can generate vastly different visualizations that lead to very different interpretations of the data. To address this issue, we propose multiple scores to quantify the "clusterness" and "trajectoriness" of single-cell RNA-seq data, in other words, whether the data looks like a collection of distinct clusters or a continuum of progression trajectory. The scores we introduce are based on pairwise distance distribution, persistent homology, vector magnitude, Ripley's K, and degrees of connectivity. Using simulated datasets, we demonstrate that the proposed scores are able to effectively differentiate between cluster-like data and trajectory-like data. Using real single-cell RNA-seq datasets, we demonstrate the scores can serve as indicators of whether clustering analysis or trajectory inference is a more appropriate choice for biological interpretation of the data.

Identifying dysregulated immune cell subsets following volumetric muscle loss with pseudo-time trajectories.

Volumetric muscle loss (VML) results in permanent functional deficits and remains a substantial regenerative medicine challenge. A coordinated immune response is crucial for timely myofiber regeneration, however the immune response following VML has yet to be fully characterized. Here, we leveraged dimensionality reduction and pseudo-time analysis techniques to elucidate the cellular players underlying a functional or pathological outcome as a result of subcritical injury or critical VML in the murine quadriceps, respectively. We found that critical VML resulted in a sustained presence of M2-like and CD206hiLy6Chi 'hybrid' macrophages whereas subcritical defects resolved these populations. Notably, the retained M2-like macrophages from critical VML injuries presented with aberrant cytokine production which may contribute to fibrogenesis, as indicated by their co-localization with fibroadipogenic progenitors (FAPs) in areas of collagen deposition within the defect. Furthermore, several T cell subpopulations were significantly elevated in critical VML compared to subcritical injuries. These results demonstrate a dysregulated immune response in critical VML that is unable to fully resolve the chronic inflammatory state and transition to a pro-regenerative microenvironment within the first week after injury. These data provide important insights into potential therapeutic strategies which could reduce the immune cell burden and pro-fibrotic signaling characteristic of VML.

Stromal STAT5-Mediated Trophic Activity Regulates Hematopoietic Niche Factors.

Signal transducer and activator of transcription 5 (STAT5a and STAT5b) are intrinsically critical for normal hematopoiesis but are also expressed in stromal cells. Here, STAT5ab knockout (KO) was generated with a variety of bone marrow hematopoietic and stromal Cre transgenic mouse strains. Vav1-Cre/+STAT5abfl/fl, the positive control for loss of multipotent hematopoietic function, surprisingly dysregulated niche factor mRNA expression, and deleted STAT5ab in CD45neg cells. Single-cell transcriptome analysis of bone marrow from Vav1-Cre/+ wild-type or Vav1-Cre/+STAT5abfl/fl mice showed hematopoietic stem cell (HSC) myeloid commitment priming. Nes+ cells were detected in both CD45neg and CD45+ clusters and deletion of STAT5ab with Nes-Cre caused hematopoietic repopulating defects. To follow up on these promiscuous Cre promoter deletions in CD45neg and CD45+ bone marrow cell populations, more stroma-specific Cre strains were generated and demonstrated a reduction in multipotent hematopoietic progenitors. Functional support for niche-supporting activity was assessed using STAT5-deficient mesenchymal stem cells (MSCs). With Lepr-Cre/+STAT5abfl/fl, niche factor mRNAs were downregulated with validation of reduced IGF-1 and CXCL12 proteins. Furthermore, advanced computational analyses revealed a key role for STAT5ab/Cish balance with Cish strongly co-expressed in MSCs and HSCs primed for differentiation. Therefore, STAT5ab-associated gene regulation supports the bone marrow microenvironment.

Quantifying Cell-Type-Specific Differences of Single-Cell Datasets Using Uniform Manifold Approximation and Projection for Dimension Reduction and Shapley Additive exPlanations.

With rapid advances in single-cell profiling technologies, larger-scale investigations that require comparisons of multiple single-cell datasets can lead to novel findings. Specifically, quantifying cell-type-specific responses to different conditions across single-cell datasets could be useful in understanding how the difference in conditions is induced at a cellular level. In this study, we present a computational pipeline that quantifies cell-type-specific differences and identifies genes responsible for the differences. We quantify differences observed in a low-dimensional uniform manifold approximation and projection for dimension reduction space as a proxy for the difference present in the high-dimensional space and use SHapley Additive exPlanations to quantify genes driving the differences. In this study, we applied our algorithm to the Iris flower dataset, single-cell RNA sequencing dataset, and mass cytometry dataset and demonstrate that it can robustly quantify cell-type-specific differences and it can also identify genes that are responsible for the differences.

Embracing enzyme promiscuity with activity-based compressed biosensing.

The development of protease-activatable drugs and diagnostics requires identifying substrates specific to individual proteases. However, this process becomes increasingly difficult as the number of target proteases increases because most substrates are promiscuously cleaved by multiple proteases. We introduce a method-substrate libraries for compressed sensing of enzymes (SLICE)-for selecting libraries of promiscuous substrates that classify protease mixtures (1) without deconvolution of compressed signals and (2) without highly specific substrates. SLICE ranks substrate libraries using a compression score (C), which quantifies substrate orthogonality and protease coverage. This metric is predictive of classification accuracy across 140 in silico (Pearson r = 0.71) and 55 in vitro libraries (r = 0.55). Using SLICE, we select a two-substrate library to classify 28 samples containing 11 enzymes in plasma (area under the receiver operating characteristic curve [AUROC] = 0.93). We envision that SLICE will enable the selection of libraries that capture information from hundreds of enzymes using fewer substrates for applications like activity-based sensors for imaging and diagnostics.

Applying interpretable machine learning workflow to evaluate exposure-response relationships for large-molecule oncology drugs.

The application of logistic regression (LR) and Cox Proportional Hazard (CoxPH) models are well-established for evaluating exposure-response (E-R) relationship in large molecule oncology drugs. However, applying machine learning (ML) models on evaluating E-R relationships has not been widely explored. We developed a workflow to train regularized LR/CoxPH and tree-based XGboost (XGB) models, and derive the odds ratios for best overall response and hazard ratios for overall survival, across exposure quantiles to evaluate the E-R relationship using clinical trial datasets. The E-R conclusions between LR/CoxPH and XGB models are overall consistent, and largely aligned with historical pharmacometric analyses findings. Overall, applying this interpretable ML workflow provides a promising alternative method to assess E-R relationships for impacting key dosing decisions in drug development.

Analyzing immune response to engineered hydrogels by hierarchical clustering of inflammatory cell subsets.

Understanding the immune response to hydrogel implantation is critical for the design of immunomodulatory biomaterials. To study the progression of inflammation around poly(ethylene glycol) hydrogels presenting Arg-Gly-Asp (RGD) peptides and vascular endothelial growth factor, we used temporal analysis of high-dimensional flow cytometry data paired with intravital imaging, immunohistochemistry, and multiplexed proteomic profiling. RGD-presenting hydrogels created a reparative microenvironment promoting CD206+ cellular infiltration and revascularization in wounded dorsal skin tissue. Unbiased clustering algorithms (SPADE) revealed significant phenotypic transition shifts as a function of the cell-adhesion hydrogel properties. SPADE identified an intermediate macrophage subset functionally regulating in vivo cytokine secretion that was preferentially recruited for RGD-presenting hydrogels, whereas dendritic cell subsets were preferentially recruited to RDG-presenting hydrogels. Last, RGD-presenting hydrogels controlled macrophage functional cytokine secretion to direct polarization and vascularization. Our studies show that unbiased clustering of single-cell data provides unbiased insights into the underlying immune response to engineered materials.

Nanofiber-Based Delivery of Bioactive Lipids Promotes Pro-regenerative Inflammation and Enhances Muscle Fiber Growth After Volumetric Muscle Loss.

Volumetric muscle loss (VML) injuries after extremity trauma results in an important clinical challenge often associated with impaired healing, significant fibrosis, and long-term pain and functional deficits. While acute muscle injuries typically display a remarkable capacity for regeneration, critically sized VML defects present a dysregulated immune microenvironment which overwhelms innate repair mechanisms leading to chronic inflammation and pro-fibrotic signaling. In this series of studies, we developed an immunomodulatory biomaterial therapy to locally modulate the sphingosine-1-phosphate (S1P) signaling axis and resolve the persistent pro-inflammatory injury niche plaguing a critically sized VML defect. Multiparameter pseudo-temporal 2D projections of single cell cytometry data revealed subtle distinctions in the altered dynamics of specific immune subpopulations infiltrating the defect that were critical to muscle regeneration. We show that S1P receptor modulation via nanofiber delivery of Fingolimod (FTY720) was characterized by increased numbers of pro-regenerative immune subsets and coincided with an enriched pool of muscle stem cells (MuSCs) within the injured tissue. This FTY720-induced priming of the local injury milieu resulted in increased myofiber diameter and alignment across the defect space followed by enhanced revascularization and reinnervation of the injured muscle. These findings indicate that localized modulation of S1P receptor signaling via nanofiber scaffolds, which resemble the native extracellular matrix ablated upon injury, provides great potential as an immunotherapy for bolstering endogenous mechanisms of regeneration following VML injury.

An mHealth App (Speech Banana) for Auditory Training: App Design and Development Study.

BACKGROUND: With the growing adult population using electronic hearing devices such as cochlear implants or hearing aids, there is an increasing worldwide need for auditory training (AT) to promote optimal device use. However, financial resources and scheduling conflicts make clinical AT infeasible. OBJECTIVE: To address this gap between need and accessibility, we primarily aimed to develop a mobile health (mHealth) app called Speech Banana for AT. The app would be substantially more affordable and portable than clinical AT; would deliver a validated training model that is reflective of modern techniques; and would track users' progress in speech comprehension, providing greater continuity between periodic in-person visits. To improve international availability, our secondary aim was to implement the English language training model into Korean as a proof of concept for worldwide usability. METHODS: A problem- and objective-centered Design Science Research Methodology approach was adopted to develop the Speech Banana app. A review of previous literature and computer-based learning programs outlined current AT gaps, whereas interviews with speech pathologists and users clarified the features that were addressed in the app. Past and present users were invited to evaluate the app via community forums and the System Usability Scale. RESULTS: Speech Banana has been implemented in English and Korean languages for iPad and web use. The app comprises 38 lessons, which include analytic exercises pairing visual and auditory stimuli, and synthetic quizzes presenting auditory stimuli only. During quizzes, users type the sentence heard, and the app provides visual feedback on performance. Users may select a male or female speaker and the volume of background noise, allowing for training with a range of frequencies and signal-to-noise ratios. There were more than 3200 downloads of the English iPad app and almost 100 downloads of the Korean app; more than 100 users registered for the web apps. The English app received a System Usability Scale rating of "good" from 6 users, and the Korean app received a rating of "OK" from 16 users. CONCLUSIONS: Speech Banana offers AT accessibility with a validated curriculum, allowing users to develop speech comprehension skills with the aid of a mobile device. This mHealth app holds potential as a supplement to clinical AT, particularly in this era of global telemedicine.

JSOM: Jointly-evolving self-organizing maps for alignment of biological datasets and identification of related clusters.

With the rapid advances of various single-cell technologies, an increasing number of single-cell datasets are being generated, and the computational tools for aligning the datasets which make subsequent integration or meta-analysis possible have become critical. Typically, single-cell datasets from different technologies cannot be directly combined or concatenated, due to the innate difference in the data, such as the number of measured parameters and the distributions. Even datasets generated by the same technology are often affected by the batch effect. A computational approach for aligning different datasets and hence identifying related clusters will be useful for data integration and interpretation in large scale single-cell experiments. Our proposed algorithm called JSOM, a variation of the Self-organizing map, aligns two related datasets that contain similar clusters, by constructing two maps-low-dimensional discretized representation of datasets-that jointly evolve according to both datasets. Here we applied the JSOM algorithm to flow cytometry, mass cytometry, and single-cell RNA sequencing datasets. The resulting JSOM maps not only align the related clusters in the two datasets but also preserve the topology of the datasets so that the maps could be used for further analysis, such as clustering.

Dual delivery of IL-10 and AT-RvD1 from PEG hydrogels polarize immune cells towards pro-regenerative phenotypes.

Inflammation after traumatic injury or surgical intervention is both a protective tissue response leading to regeneration and a potential cause of wound complications. One potentially successful strategy to harness to pro-regenerative roles of host inflammation is the localized delivery of bioactive materials to induce immune suppressive cellular responses by cells responding to injury. In this study, we designed a fully synthetic poly (ethylene) glycol (PEG)-based hydrogel to release the specialized pro-resolving lipid mediator aspirin-triggered resolvin-D1 (AT-RvD1) and recombinant human interleukin 10 (IL-10). We utilized a unique side-by-side internally controlled implant design wherein bioactive hydrogels were implanted adjacent to control hydrogels devoid of immune modulatory factors in the dorsal skinfold window chamber. We also explored single-immune cell data with unsupervised approaches such as SPADE. First, we show that RGD-presenting hydrogel delivery results in enhanced immune cell recruitment to the site of injury. We then use intra-vital imaging to assess cellular recruitment and microvascular remodeling to show an increase in the caliber and density of local microvessels. Finally, we show that the recruitment and re-education of mononuclear phagocytes by combined delivery IL-10 and AT-RvD1 localizes immune suppressive subsets to the hydrogel, including CD206+ macrophages (M2a/c) and IL-10 expressing dendritic cells in the context of chronic inflammation following surgical tissue disruption. These data demonstrate the potential of combined delivery on the recruitment of regenerative cell subsets involved in wound healing complications.

Modulating local S1P receptor signaling as a regenerative immunotherapy after volumetric muscle loss injury.

Regeneration of skeletal muscle after volumetric injury is thought to be impaired by a dysregulated immune microenvironment that hinders endogenous repair mechanisms. Such defects result in fatty infiltration, tissue scarring, chronic inflammation, and debilitating functional deficits. Here, we evaluated the key cellular processes driving dysregulation in the injury niche through localized modulation of sphingosine-1-phosphate (S1P) receptor signaling. We employ dimensionality reduction and pseudotime analysis on single cell cytometry data to reveal heterogeneous immune cell subsets infiltrating preclinical muscle defects due to S1P receptor inhibition. We show that global knockout of S1P receptor 3 (S1PR3) is marked by an increase of muscle stem cells within injured tissue, a reduction in classically activated relative to alternatively activated macrophages, and increased bridging of regenerating myofibers across the defect. We found that local S1PR3 antagonism via nanofiber delivery of VPC01091 replicated key features of pseudotime immune cell recruitment dynamics and enhanced regeneration characteristic of global S1PR3 knockout. Our results indicate that local S1P receptor modulation may provide an effective immunotherapy for promoting a proreparative environment leading to improved regeneration following muscle injury.

Risk-associated alterations in marrow T cells in pediatric leukemia.

Current management of childhood leukemia is tailored based on disease risk determined by clinical features at presentation. Whether properties of the host immune response impact disease risk and outcome is not known. Here, we combine mass cytometry, single cell genomics, and functional studies to characterize the BM immune environment in children with B cell acute lymphoblastic leukemia and acute myelogenous leukemia at presentation. T cells in leukemia marrow demonstrate evidence of chronic immune activation and exhaustion/dysfunction, with attrition of naive T cells and TCF1+ stem-like memory T cells and accumulation of terminally differentiated effector T cells. Marrow-infiltrating NK cells also exhibit evidence of dysfunction, particularly in myeloid leukemia. Properties of immune cells identified distinct immune phenotype-based clusters correlating with disease risk in acute lymphoblastic leukemia. High-risk immune signatures were associated with expression of stem-like genes on tumor cells. These data provide a comprehensive assessment of the immune landscape of childhood leukemias and identify targets potentially amenable to therapeutic intervention. These studies also suggest that properties of the host response with depletion of naive T cells and accumulation of terminal-effector T cells may contribute to the biologic basis of disease risk. Properties of immune microenvironment identified here may also impact optimal application of immune therapies, including T cell-redirection approaches in childhood leukemia.

Correlation Patterns Between DNA Methylation and Gene Expression in The Cancer Genome Atlas.

BACKGROUND: DNA methylation is a form of epigenetic modification that has been shown to play a significant role in gene regulation. In cancer, DNA methylation plays an important role by regulating the expression of oncogenes. The role of DNA methylation in the onset and progression of various cancer types is now being elucidated as more large-scale data become available. The Cancer Genome Atlas (TCGA) provides a wealth of information for the analysis of various molecular aspects of cancer genetics. Gene expression data and DNA methylation data from TCGA have been used for a variety of studies. A traditional understanding of the effects of DNA methylation on gene expression has linked methylation of CpG sites in the gene promoter region with the decrease in gene expression. Recent studies have begun to expand this traditional role of DNA methylation. RESULTS: Here we present a pan-cancer analysis of correlation patterns between CpG methylation and gene expression. Using matching patient data from TCGA, 33 cancer-specific correlations were calculated for each CpG site and the expression level of its corresponding gene. These correlations were used to identify patterns on a per-site basis as well as patterns of methylation across the gene body. Using these identified patterns, we found genes that contain conflicting methylation signals beyond the commonly accepted association between the promoter region methylation and silencing of gene expression. Beyond gene body methylation in whole, we examined individual CpG sites and show that, even in the same gene body, some sites can have a contradictory effect on gene expression in cancers. CONCLUSIONS: We observed that within promoter regions there was a substantial amount of positive correlation between methylation and gene expression, which contradicts the commonly accepted association. We observed that the correlation between CpG methylation and gene expression does not exhibit in a tissue-specific manner, suggesting that the effects of methylation on gene expression are largely tissue independent. The analysis of correlation associated with the location of the CpG site in the gene body has led to the identification of several different methylation patterns that affect gene expression, and several examples of methylation activating gene expression were observed. Distinctly opposing or conflicting effects were seen in close proximity on the gene body, where negative and positive correlations were seen at the neighboring CpG sites.

Quantification and tracking of genetically engineered dendritic cells for studying immunotherapy.

PURPOSE: Genetically encoded reporters can assist in visualizing biological processes in live organisms and have been proposed for longitudinal and noninvasive tracking of therapeutic cells in deep tissue. Cells can be labeled in situ or ex vivo and followed in live subjects over time. Nevertheless, a major challenge for reporter systems is to identify the cell population that actually expresses an active reporter. METHODS: We have used a nucleoside analog, pyrrolo-2'-deoxycytidine, as an imaging probe for the putative reporter gene, Drosophila melanogaster 2'-deoxynucleoside kinase. Bioengineered cells were imaged in vivo in animal models of brain tumor and immunotherapy using chemical exchange saturation transfer MRI. The number of transduced cells was quantified by flow cytometry based on the optical properties of the probe. RESULTS: We performed a comparative analysis of six different cell lines and demonstrate utility in a mouse model of immunotherapy. The proposed technology can be used to quantify the number of labeled cells in a given region, and moreover is sensitive enough to detect less than 10,000 cells. CONCLUSION: This unique technology that enables efficient selection of labeled cells followed by in vivo monitoring with both optical and MRI. Magn Reson Med 79:1010-1019, 2018. © 2017 International Society for Magnetic Resonance in Medicine.