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Inference of causal transcriptional regulatory networks (TRNs) from transcriptomic data suffers notoriously from false positives. Approaches to control the false discovery rate (FDR), for example, via permutation, bootstrapping, or multivariate Gaussian distributions, suffer from several complications: difficulty in distinguishing direct from indirect regulation, nonlinear effects, and causal structure inference requiring "causal sufficiency," meaning experiments that are free of any unmeasured, confounding variables. Here, we use a recently developed statistical framework, model-X knockoffs, to control the FDR while accounting for indirect effects, nonlinear dose-response, and user-provided covariates. We adjust the procedure to estimate the FDR correctly even when measured against incomplete gold standards. However, benchmarking against chromatin immunoprecipitation (ChIP) and other gold standards reveals higher observed than reported FDR. This indicates that unmeasured confounding is a major driver of FDR in TRN inference. A record of this paper's transparent peer review process is included in the supplemental information.

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Methods that predict fate potential or degree of differentiation from transcriptomic data have identified rare progenitor populations and uncovered developmental regulatory mechanisms. However, some state-of-the-art methods are too computationally burdensome for emerging large-scale data and all methods make inaccurate predictions in certain biological systems. We developed a method in R (stemFinder) that predicts single cell differentiation time based on heterogeneity in cell cycle gene expression. Our method is computationally tractable and is as good as or superior to competitors. As part of our benchmarking, we implemented four different performance metrics to assist potential users in selecting the tool that is most apt for their application. Finally, we explore the relationship between differentiation time and cell fate potential by analyzing a lineage tracing dataset with clonally labelled hematopoietic cells, revealing that metrics of differentiation time are correlated with the number of downstream lineages.

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Polyamine metabolism and signaling play important roles in multiple cancers but have not previously been studied in Ewing sarcoma. Here, we show that blocking polyamine synthesis with D, L-alpha-difluoromethylornithine (DFMO) causes a G1 cell cycle arrest, dose-dependent decreases in sarcosphere formation from Ewing sarcoma cell lines growing in non-adherent conditions and a decrease in clonogenic growth in soft agar. Further, we utilized our orthotopic implantation/amputation model of Ewing sarcoma metastasis to demonstrate that DFMO slowed primary tumor growth in addition to limiting metastasis. RNA sequencing demonstrated gene expression patterns consistent with induction of ferroptosis caused by polyamine depletion. Induction of ferroptosis was validated in vitro by demonstrating that ferrostatin-1, an inhibitor of ferroptosis, allows sphere formation even in the presence of DFMO. Collectively, these results reveal a novel mechanism by which DFMO prevents metastasis - induction of ferroptosis due to polyamine depletion. Our results provide preclinical justification to test the ability of DFMO to prevent metastatic recurrence in Ewing sarcoma patients at high risk for relapse.

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Computational modelling of cell state transitions has been a great interest of many in the field of developmental biology, cancer biology and cell fate engineering because it enables performing perturbation experiments in silico more rapidly and cheaply than could be achieved in a wet lab. Recent advancements in single-cell RNA sequencing (scRNA-seq) allow the capture of high-resolution snapshots of cell states as they transition along temporal trajectories. Using these high-throughput datasets, we can train computational models to generate in silico 'synthetic' cells that faithfully mimic the temporal trajectories. Here we present OneSC, a platform that can simulate synthetic cells across developmental trajectories using systems of stochastic differential equations govern by a core transcription factors (TFs) regulatory network. Different from the current network inference methods, OneSC prioritizes on generating Boolean network that produces faithful cell state transitions and steady cell states that mimic real biological systems. Applying OneSC to real data, we inferred a core TF network using a mouse myeloid progenitor scRNA-seq dataset and showed that the dynamical simulations of that network generate synthetic single-cell expression profiles that faithfully recapitulate the four myeloid differentiation trajectories going into differentiated cell states (erythrocytes, megakaryocytes, granulocytes and monocytes). Finally, through the in-silico perturbations of the mouse myeloid progenitor core network, we showed that OneSC can accurately predict cell fate decision biases of TF perturbations that closely match with previous experimental observations.

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The profound pain accompanying bone fracture is mediated by somatosensory neurons, which also appear to be required to initiate bone regeneration following fracture. Surprisingly, the precise neuroanatomical circuitry mediating skeletal nociception and regeneration remains incompletely understood. Here, we characterized somatosensory dorsal root ganglia (DRG) afferent neurons innervating murine long bones before and after experimental long bone fracture in mice. Retrograde labeling of DRG neurons by an adeno-associated virus with peripheral nerve tropism showed AAV-tdT signal. Single cell transcriptomic profiling of 6,648 DRG neurons showed highest labeling across CGRP+ neuron clusters (6.9-17.2%) belonging to unmyelinated C fibers, thinly myelinated Aδ fibers and Aß-Field LTMR (9.2%). Gene expression profiles of retrograde labeled DRG neurons over multiple timepoints following experimental stress fracture revealed dynamic changes in gene expression corresponding to the acute inflammatory ( S100a8 , S100a9 ) and mechanical force ( Piezo2 ). Reparative phase after fracture included morphogens such as Tgfb1, Fgf9 and Fgf18 . Two methods to surgically or genetically denervate fractured bones were used in combination with scRNA-seq to implicate defective mesenchymal cell proliferation and osteodifferentiation as underlying the poor bone repair capacity in the presence of attenuated innervation. Finally, multi-tissue scRNA-seq and interactome analyses implicated neuron-derived FGF9 as a potent regulator of fracture repair, a finding compatible with in vitro assessments of neuron-to-skeletal mesenchyme interactions.

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Single-cell methods offer a high-resolution approach for characterizing cell populations. Many studies rely on single-cell transcriptomics to draw conclusions regarding cell state and behavior, with the underlying assumption that transcriptomic readouts largely parallel their protein counterparts and subsequent activity. However, the relationship between transcriptomic and proteomic measurements is imprecise, and thus datasets that probe the extent of their concordance will be useful to refine such conclusions. Additionally, novel single-cell analysis tools often lack appropriate gold standard datasets for the purposes of assessment. Integrative (combining the two data modalities) and predictive (using one modality to improve results from the other) approaches in particular, would benefit from transcriptomic and proteomic data from the same sample of cells. For these reasons, we performed single-cell RNA sequencing, mass cytometry, and flow cytometry on a split-sample of human peripheral blood mononuclear cells. We directly compare the proportions of specific cell types resolved by each technique, and further describe the extent to which protein and mRNA measurements correlate within distinct cell types.

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The mechanisms by which bone marrow stromal cells (BMSCs) maintain multilineage potency in vitro remain elusive. To identify the transcriptional regulatory circuits that contribute to BMSC multipotency, we performed paired single-nucleus multiomics of the expansion of freshly isolated BMSCs and of BMSCs undergoing tri-lineage differentiation. By computationally reconstructing the regulatory programs associated with initial stages of differentiation and early expansion, we identified the TEAD family of transcription factors, which is inhibited by Hippo signaling, as highly active in the BMSC in vitro multipotent state. Pharmacological inhibition of TEAD enhanced BMSC osteogenic and adipogenic differentiation, whereas its activation maintained BMSCs in an undifferentiated state, supporting a model whereby isolation of BMSCs coincides with a TEAD-controlled transcriptional state linked to multipotency. Our study highlights the Hippo pathway as a pivotal regulator of BMSC multipotency, and our regulatory network inferences are a reservoir of testable hypotheses that link transcription factors and their regulons to specific aspects of BMSC behavior.

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To gain insight into the transcription programs activated during the formation of Drosophila larval structures, we carried out single cell RNA sequencing during two periods of Drosophila embryogenesis: stages 10-12, when most organs are first specified and initiate morphological and physiological specialization; and stages 13-16, when organs achieve their final mature architectures and begin to function. Our data confirm previous findings with regards to functional specialization of some organs - the salivary gland and trachea - and clarify the embryonic functions of another - the plasmatocytes. We also identify two early developmental trajectories in germ cells and uncover a potential role for proteolysis during germline stem cell specialization. We identify the likely cell type of origin for key components of the Drosophila matrisome and several commonly used Drosophila embryonic cell culture lines. Finally, we compare our findings with other recent related studies and with other modalities for identifying tissue-specific gene expression patterns. These data provide a useful community resource for identifying many new players in tissue-specific morphogenesis and functional specialization of developing organs.

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Acute kidney injury (AKI) is a major risk factor for long-term adverse outcomes, including chronic kidney disease. In mouse models of AKI, maladaptive repair of the injured proximal tubule (PT) prevents complete tissue recovery. However, evidence for PT maladaptation and its etiological relationship with complications of AKI is lacking in humans. We performed single-nucleus RNA sequencing of 120,985 nuclei in kidneys from 17 participants with AKI and seven healthy controls from the Kidney Precision Medicine Project. Maladaptive PT cells, which exhibited transcriptomic features of dedifferentiation and enrichment in pro-inflammatory and profibrotic pathways, were present in participants with AKI of diverse etiologies. To develop plasma markers of PT maladaptation, we analyzed the plasma proteome in two independent cohorts of patients undergoing cardiac surgery and a cohort of marathon runners, linked it to the transcriptomic signatures associated with maladaptive PT, and identified nine proteins whose genes were specifically up- or down-regulated by maladaptive PT. After cardiac surgery, both cohorts of patients had increased transforming growth factor-ß2 (TGFB2), collagen type XXIII-α1 (COL23A1), and X-linked neuroligin 4 (NLGN4X) and had decreased plasminogen (PLG), ectonucleotide pyrophosphatase/phosphodiesterase 6 (ENPP6), and protein C (PROC). Similar changes were observed in marathon runners with exercise-associated kidney injury. Postoperative changes in these markers were associated with AKI progression in adults after cardiac surgery and post-AKI kidney atrophy in mouse models of ischemia-reperfusion injury and toxic injury. Our results demonstrate the feasibility of a multiomics approach to discovering noninvasive markers and associating PT maladaptation with adverse clinical outcomes.

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T cells are important in the pathogenesis of acute kidney injury (AKI), and TCR+CD4-CD8- (double negative-DN) are T cells that have regulatory properties. However, there is limited information on DN T cells compared to traditional CD4+ and CD8+ cells. To elucidate the molecular signature and spatial dynamics of DN T cells during AKI, we performed single-cell RNA sequencing (scRNA-seq) on sorted murine DN, CD4+, and CD8+ cells combined with spatial transcriptomic profiling of normal and post AKI mouse kidneys. scRNA-seq revealed distinct transcriptional profiles for DN, CD4+, and CD8+ T cells of mouse kidneys with enrichment of Kcnq5, Klrb1c, Fcer1g, and Klre1 expression in DN T cells compared to CD4+ and CD8+ T cells in normal kidney tissue. We validated the expression of these four genes in mouse kidney DN, CD4+ and CD8+ T cells using RT-PCR and Kcnq5, Klrb1, and Fcer1g genes with the NIH human kidney precision medicine project (KPMP). Spatial transcriptomics in normal and ischemic mouse kidney tissue showed a localized cluster of T cells in the outer medulla expressing DN T cell genes including Fcer1g. These results provide a template for future studies in DN T as well as CD4+ and CD8+ cells in normal and diseased kidneys.

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Due to the abundance of single cell RNA-seq data, a number of methods for predicting expression after perturbation have recently been published. Expression prediction methods are enticing because they promise to answer pressing questions in fields ranging from developmental genetics to cell fate engineering and because they are faster, cheaper, and higher-throughput than their experimental counterparts. However, the absolute and relative accuracy of these methods is poorly characterized, limiting their informed use, their improvement, and the interpretation of their predictions. To address these issues, we created a benchmarking platform that combines a panel of large-scale perturbation datasets with an expression forecasting software engine that encompasses or interfaces to current methods. We used our platform to systematically assess methods, parameters, and sources of auxiliary data. We found that uninformed baseline predictions, which were not always included in prior evaluations, yielded the same or better mean absolute error than benchmarked methods in all test cases. These results cast doubt on the ability of current expression forecasting methods to provide mechanistic insights or to rank hypotheses for experimental follow-up. However, given the rapid pace of innovation in the field, new approaches may yield more accurate expression predictions. Our platform will serve as a neutral benchmark to improve methods and to identify contexts in which expression prediction can succeed.

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Optimization of cell engineering protocols requires standard, comprehensive quality metrics. We previously developed CellNet, a computational tool to quantitatively assess the transcriptional fidelity of engineered cells compared with their natural counterparts, based on bulk-derived expression profiles. However, this platform and others were limited in their ability to compare data from different sources, and no current tool makes it easy to compare new protocols with existing state-of-the-art protocols in a standardized manner. Here, we utilized our prior application of the top-scoring pair transformation to build a computational platform, platform-agnostic CellNet (PACNet), to address both shortcomings. To demonstrate the utility of PACNet, we applied it to thousands of samples from over 100 studies that describe dozens of protocols designed to produce seven distinct cell types. We performed an in-depth examination of hepatocyte and cardiomyocyte protocols to identify the best-performing methods, characterize the extent of intra-protocol and inter-lab variation, and identify common off-target signatures, including a surprising neural/neuroendocrine signature in primary liver-derived organoids. We have made PACNet available as an easy-to-use web application, allowing users to assess their protocols relative to our database of reference engineered samples, and as open-source, extensible code.

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Bone undergoes constant remodeling by osteoclast bone resorption coupled with osteoblast bone formation at the bone surface. A third major cell type in the bone is osteocytes, which are embedded in the matrix, are well-connected to the lacunar network, and are believed to act as mechanical sensors. Here, it is reported that sympathetic innervation directly regulates lacunar osteocyte-mediated bone resorption inside cortical bone. It is found that sympathetic activity is elevated in different mouse models of bone loss, including lactation, ovariectomy, and glucocorticoid treatment. Further, during lactation elevated sympathetic outflow induces netrin-1 expression by osteocytes to further promote sympathetic nerve sprouting in the cortical bone endosteum in a feed-forward loop. Depletion of tyrosine hydroxylase-positive (TH+ ) sympathetic nerves ameliorated osteocyte-mediated perilacunar bone resorption in lactating mice. Moreover, norepinephrine activates ß-adrenergic receptor 2 (Adrb2) signaling to promote secretion of extracellular vesicles (EVs) containing bone-degrading enzymes for perilacunar bone resorption and inhibit osteoblast differentiation. Importantly, osteocyte-specific deletion of Adrb2 or treatment with a ß-blocker results in lower bone resorption in lactating mice. Together, these findings show that the sympathetic nervous system promotes osteocyte-driven bone loss during lactation, likely as an adaptive response to the increased energy and mineral demands of the nursing mother.

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Joint destruction is the major clinic burden in patients with rheumatoid arthritis (RA). It is unclear, though, how this autoimmune disease progresses to the point of deterioration of the joint. Here, we report that in a mouse model of RA the upregulation of TLR2 expression and its α(2,3) sialylation in RANK+ myeloid monocytes mediate the transition from autoimmunity to osteoclast fusion and bone resorption, resulting in joint destruction. The expression of α(2,3) sialyltransferases was significantly increased in RANK+TLR2+ myeloid monocytes, and their inhibition or treatment with a TLR2 inhibitor blocked osteoclast fusion. Notably, analysis of our single-cell RNA-sequencing (scRNA-seq) libraries generated from RA mice revealed a novel RANK+TLR2- a subset that negatively regulated osteoclast fusion. Importantly, the RANK+TLR2+ subset was significantly diminished with the treatments, whereas the RANK+TLR2- subset was expanded. Moreover, the RANK+TLR2- subset could differentiate into a TRAP+ osteoclast lineage, but the resulting cells did not fuse to form osteoclasts. Our scRNA-seq data showed that Maf is highly expressed in the RANK+TLR2- subset, and the α(2,3) sialyltransferase inhibitor-induced Maf expression in the RANK+TLR2+ subset. The identification of a RANK+TLR2- subset provides a potential explanation for TRAP+ mononuclear cells in bone and their anabolic activity. Further, TLR2 expression and its α(2,3) sialylation in the RANK+ myeloid monocytes could be effective targets to prevent autoimmune-mediated joint destruction.

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BACKGROUND: A cell exhibits a variety of responses to internal and external cues. These responses are possible, in part, due to the presence of an elaborate gene regulatory network (GRN) in every single cell. In the past 20 years, many groups worked on reconstructing the topological structure of GRNs from large-scale gene expression data using a variety of inference algorithms. Insights gained about participating players in GRNs may ultimately lead to therapeutic benefits. Mutual information (MI) is a widely used metric within this inference/reconstruction pipeline as it can detect any correlation (linear and non-linear) between any number of variables (n-dimensions). However, the use of MI with continuous data (for example, normalized fluorescence intensity measurement of gene expression levels) is sensitive to data size, correlation strength and underlying distributions, and often requires laborious and, at times, ad hoc optimization. RESULTS: In this work, we first show that estimating MI of a bi- and tri-variate Gaussian distribution using k-nearest neighbor (kNN) MI estimation results in significant error reduction as compared to commonly used methods based on fixed binning. Second, we demonstrate that implementing the MI-based kNN Kraskov-Stoögbauer-Grassberger (KSG) algorithm leads to a significant improvement in GRN reconstruction for popular inference algorithms, such as Context Likelihood of Relatedness (CLR). Finally, through extensive in-silico benchmarking we show that a new inference algorithm CMIA (Conditional Mutual Information Augmentation), inspired by CLR, in combination with the KSG-MI estimator, outperforms commonly used methods. CONCLUSIONS: Using three canonical datasets containing 15 synthetic networks, the newly developed method for GRN reconstruction-which combines CMIA, and the KSG-MI estimator-achieves an improvement of 20-35% in precision-recall measures over the current gold standard in the field. This new method will enable researchers to discover new gene interactions or better choose gene candidates for experimental validations.

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SIGNIFICANCE STATEMENT: T cells mediate pathogenic and reparative processes during AKI, but the exact mechanisms regulating kidney T cell functions are unclear. This study identified upregulation of the novel immune checkpoint molecule, TIGIT, on mouse and human kidney T cells after AKI. TIGIT-expressing kidney T cells produced proinflammatory cytokines and had effector (EM) and central memory (CM) phenotypes. TIGIT-deficient mice had protection from both ischemic and nephrotoxic AKI. Single-cell RNA sequencing led to the discovery of possible downstream targets of TIGIT. TIGIT mediates AKI pathophysiology, is a promising novel target for AKI therapy, and is being increasingly studied in human cancer therapy trials. BACKGROUND: T cells play pathogenic and reparative roles during AKI. However, mechanisms regulating T cell responses are relatively unknown. We investigated the roles of the novel immune checkpoint molecule T cell immunoreceptor with Ig and immunoreceptor tyrosine-based inhibitory motif domains (TIGIT) in kidney T cells and AKI outcomes. METHODS: TIGIT expression and functional effects were evaluated in mouse kidney T cells using RNA sequencing (RNA-Seq) and flow cytometry. TIGIT effect on AKI outcomes was studied with TIGIT knockout (TIGIT-KO) mice in ischemia reperfusion (IR) and cisplatin AKI models. Human kidney T cells from nephrectomy samples and single cell RNA sequencing (scRNA-Seq) data from the Kidney Precision Medicine Project were used to assess TIGIT's role in humans. RESULTS: RNA-Seq and flow cytometry analysis of mouse kidney CD4+ T cells revealed increased expression of TIGIT after IR injury. Ischemic injury also increased TIGIT expression in human kidney T cells, and TIGIT expression was restricted to T/natural killer cell subsets in patients with AKI. TIGIT-expressing kidney T cells in wild type (WT) mice had an effector/central memory phenotype and proinflammatory profile at baseline and post-IR. Kidney regulatory T cells were predominantly TIGIT+ and significantly reduced post-IR. TIGIT-KO mice had significantly reduced kidney injury after IR and nephrotoxic injury compared with WT mice. scRNA-Seq analysis showed enrichment of genes related to oxidative phosphorylation and mTORC1 signaling in Th17 cells from TIGIT-KO mice. CONCLUSIONS: TIGIT expression increases in mouse and human kidney T cells during AKI, worsens AKI outcomes, and is a novel therapeutic target for AKI.

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Tissue engineering strategies that combine human pluripotent stem cell-derived myogenic progenitors (hPDMs) with advanced biomaterials provide promising tools for engineering 3D skeletal muscle grafts to model tissue development in vitro and promote muscle regeneration in vivo. We recently demonstrated (i) the potential for obtaining large numbers of hPDMs using a combination of two small molecules without the overexpression of transgenes and (ii) the application of electrospun fibrin microfiber bundles for functional skeletal muscle restoration following volumetric muscle loss. In this study, we aimed to demonstrate that the biophysical cues provided by the fibrin microfiber bundles induce hPDMs to form engineered human skeletal muscle grafts containing multinucleated myotubes that express desmin and myosin heavy chains and that these grafts could promote regeneration following skeletal muscle injuries. We tested a genetic PAX7 reporter line (PAX7::GFP) to sort for more homogenous populations of hPDMs. RNA sequencing and gene set enrichment analyses confirmed that PAX7::GFP-sorted hPDMs exhibited high expression of myogenic genes. We tested engineered human skeletal muscle grafts derived from PAX7::GFP-sorted hPDMs within in vivo skeletal muscle defects by assessing myogenesis, engraftment and immunogenicity using immunohistochemical staining. The PAX7::GFP-sorted groups had moderately high vascular infiltration and more implanted cell association with embryonic myosin heavy chain (eMHC) regions, suggesting they induced pro-regenerative microenvironments. These findings demonstrated the promise for the use of PAX7::GFP-sorted hPDMs on fibrin microfiber bundles and provided some insights for improving the cell-biomaterial system to stimulate more robust in vivo skeletal muscle regeneration.

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Elucidating regulatory relationships between transcription factors (TFs) and target genes is fundamental to understanding how cells control their identity and behavior. Unfortunately, existing computational gene regulatory network (GRN) reconstruction methods are imprecise, computationally burdensome, and fail to reveal dynamic regulatory topologies. Here, we present Epoch, a reconstruction tool that uses single-cell transcriptomics to accurately infer dynamic networks. We apply Epoch to identify the dynamic networks underpinning directed differentiation of mouse embryonic stem cells (ESCs) guided by multiple signaling pathways, and we demonstrate that modulating these pathways drives topological changes that bias cell fate potential. We also find that Peg3 rewires the pluripotency network to favor mesoderm specification. By integrating signaling pathways with GRNs, we trace how Wnt activation and PI3K suppression govern mesoderm and endoderm specification, respectively. Finally, we identify regulatory circuits of patterning and axis formation that distinguish in vitro and in vivo mesoderm specification.

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Vascular endothelial cell (EC) plasticity plays a critical role in the progression of atherosclerosis by giving rise to mesenchymal phenotypes in the plaque lesion. Despite the evidence for arterial stiffening as a major contributor to atherosclerosis, the complex interplay among atherogenic stimuli in vivo has hindered attempts to determine the effects of extracellular matrix (ECM) stiffness on endothelial-mesenchymal transition (EndMT). To study the regulatory effects of ECM stiffness on EndMT, an in vitro model is developed in which human coronary artery ECs are cultured on physiological or pathological stiffness substrates. Leveraging single-cell RNA sequencing, cell clusters with mesenchymal transcriptional features are identified to be more prevalent on pathological substrates than physiological substrates. Trajectory inference analyses reveal a novel mesenchymal-to-endothelial reverse transition, which is blocked by pathological stiffness substrates, in addition to the expected EndMT trajectory. ECs pushed to a mesenchymal character by pathological stiffness substrates are enriched in transcriptional signatures of atherosclerotic ECs from human and murine plaques. This study characterizes at single-cell resolution the transcriptional programs that underpin EC plasticity in both physiological or pathological milieus, and thus serves as a valuable resource for more precisely defining EndMT and the transcriptional programs contributing to atherosclerosis.