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Single-cell analysis is a rapidly evolving approach to characterize genome-scale molecular information at the individual cell level. Development of single-cell technologies and computational methods has enabled systematic investigation of cellular heterogeneity in a wide range of tissues and cell populations, yielding fresh insights into the composition, dynamics, and regulatory mechanisms of cell states in development and disease. Despite substantial advances, significant challenges remain in the analysis, integration, and interpretation of single-cell omics data. Here, we discuss the state of the field and recent advances and look to future opportunities.

The blood cancer T cell large granular lymphocyte (T-LGL) leukemia is a chronic disease characterized by a clonal proliferation of cytotoxic T cells. As no curative therapy is yet known for this disease, identification of potential therapeutic targets is of immense importance. In this paper, we perform a comprehensive dynamical and structural analysis of a network model of this disease. By employing a network reduction technique, we identify the stationary states (fixed points) of the system, representing normal and diseased (T-LGL) behavior, and analyze their precursor states (basins of attraction) using an asynchronous Boolean dynamic framework. This analysis identifies the T-LGL states of 54 components of the network, out of which 36 (67%) are corroborated by previous experimental evidence and the rest are novel predictions. We further test and validate one of these newly identified states experimentally. Specifically, we verify the prediction that the node SMAD is over-active in leukemic T-LGL by demonstrating the predominant phosphorylation of the SMAD family members Smad2 and Smad3. Our systematic perturbation analysis using dynamical and structural methods leads to the identification of 19 potential therapeutic targets, 68% of which are corroborated by experimental evidence. The novel therapeutic targets provide valuable guidance for wet-bench experiments. In addition, we successfully identify two new candidates for engineering long-lived T cells necessary for the delivery of virus and cancer vaccines. Overall, this study provides a bird's-eye-view of the avenues available for identification of therapeutic targets for similar diseases through perturbation of the underlying signal transduction network.

Boolean models, wherein each component is characterized with a binary (ON or OFF) variable, have been widely employed for dynamic modeling of biological regulatory networks. However, the exponential dependence of the size of the state space of these models on the number of nodes in the network can be a daunting prospect for attractor analysis of large-scale systems. We have previously proposed a network reduction technique for Boolean models and demonstrated its applicability on two biological systems, namely, the abscisic acid signal transduction network and the T-LGL leukemia survival signaling network. In this paper, we provide a rigorous mathematical proof that this method not only conserves the fixed points of a Boolean network, but also conserves the complex attractors of general asynchronous Boolean models wherein at each time step a randomly selected node is updated. This method thus allows one to infer the long-term dynamic properties of a large-scale system from those of the corresponding reduced model. [PUBLICATION ABSTRACT]

Background Genetic aberrations are among the critical driving factors of lung cancer. Importantly, the impact of genetic variations on proteomic dysregulations with the goal of characterizing potential diagnostic biomarkers at the population-level requires additional investigation. Modeling such proteogenomic interactions is crucial in understanding early-stage biological disruptions to inform biomarker discovery, successful clinical trials, and developing effective therapeutics. Methods We investigated two complementary aspects of lung cancer risk. First, we performed a genome-wide association study of lung cancer using population-scale datasets, then examined whether lung cancer risk-associated variants influence plasma protein levels using the UK Biobank Pharma Proteomics Project data. Second, we identified plasma proteomic dysregulations in presymptomatic and symptomatic patients with the objective of pinpointing diagnostic biomarkers through leveraging machine learning methods. Results Using the identified proteins, machine learning models achieved median cross-validated AUCs of 0.85–0.88 (0–4 years before diagnosis [YBD]), 0.81–0.84 (5–9 YBD), and 0.80–0.86 (0–9 YBD). Performing survival analyses within the 5–9 YBD group, elevated levels of eight proteins, such as CALCB, PLAUR, and CD74, were found to significantly associate with lower survival. We identified 22 disease-associated proteins, of which 14 have been previously implicated in lung cancer, including CEACAM5, CXCL17, GDF15, WFDC2 along with 8 novel proteins. These proteins were enriched in pathways related to cytokine signaling, interleukin regulation, neutrophil degranulation, and lung fibrosis. Conclusions While these findings do not establish mechanistic causality, they highlight proteomic alterations reflecting systemic changes preceding the diagnosis. Our study contributes to understanding genome–proteome relationships in lung cancer and identifies circulating proteins warranting further investigation as potential early biomarkers for screening and risk stratification. Plain Language Summary Lung cancer is the leading cause of cancer-related deaths worldwide and is often diagnosed at late stage, when treatment is less effective. Early detection is essential but challenging, as symptoms typically appear only in advanced stages of the disease. In this study, we analyzed genetic data and proteins in blood collected years before a lung cancer diagnosis. We identified 22 proteins whose levels change years before lung cancer onset. These proteins are linked to the immune system, inflammation, and lung tissue changes. Our findings suggest that blood-based markers may help support earlier lung cancer detection and improve risk assessment and could inform future strategies for disease monitoring and prevention. Johnson et al. investigate genetic variants linked to lung cancer risk and plasma proteins predicting future diagnosis. Protein levels in blood change up to 9 years before disease onset, highlighting 22 proteins involved in immune signalling, inflammation, and long fibrosis.

Little is known about how pro-obesity diets regulate tissue stem and progenitor cell function. Here we show that high-fat diet (HFD)-induced obesity augments the numbers and function of Lgr5 + intestinal stem cells of the mammalian intestine. Mechanistically, a HFD induces a robust peroxisome proliferator-activated receptor delta (PPAR-δ) signature in intestinal stem cells and progenitor cells (non-intestinal stem cells), and pharmacological activation of PPAR-δ recapitulates the effects of a HFD on these cells. Like a HFD, ex vivo treatment of intestinal organoid cultures with fatty acid constituents of the HFD enhances the self-renewal potential of these organoid bodies in a PPAR-δ-dependent manner. Notably, HFD- and agonist-activated PPAR-δ signalling endow organoid-initiating capacity to progenitors, and enforced PPAR-δ signalling permits these progenitors to form in vivo tumours after loss of the tumour suppressor Apc . These findings highlight how diet-modulated PPAR-δ activation alters not only the function of intestinal stem and progenitor cells, but also their capacity to initiate tumours. A high-fat diet increases the number of intestinal stem cells in mammals, both in vivo and in intestinal organoids; a pathway that involves PPAR-δ confers organoid-initiating capacity to non-stem cells and induces them to form in vivo tumours after loss of the Apc tumour suppressor. Metabolic effects of a pro-obesity diet How obesity-inducing diets modulate tissue stem cell function and influence pathologies such as cancer is not clear. This study shows that a high-fat diet increases the number of intestinal stem cells in mammals in vivo and in intestinal organoids treated with fatty acids. The authors find that a pathway involving peroxisome proliferator-activated receptor delta (PPAR-δ) confers organoid-initiating capacity to non-stem cells, and demonstrate that the pathway induces non-stem cells to form tumors in vivo following the loss of the Apc tumour suppressor.

Shwachman-Diamond Syndrome (SDS) is a rare and clinically-heterogeneous bone marrow (BM) failure syndrome caused by mutations in the Shwachman-Bodian-Diamond Syndrome (SBDS) gene. Although SDS was described over 50 years ago, the molecular pathogenesis is poorly understood due, in part, to the rarity and heterogeneity of the affected hematopoietic progenitors. To address this, we used single cell RNA sequencing to profile scant hematopoietic stem and progenitor cells from SDS patients. We generated a single cell map of early lineage commitment and found that SDS hematopoiesis was left-shifted with selective loss of granulocyte-monocyte progenitors. Transcriptional targets of transforming growth factor-beta (TGFβ) were dysregulated in SDS hematopoietic stem cells and multipotent progenitors, but not in lineage-committed progenitors. TGFβ inhibitors (AVID200 and SD208) increased hematopoietic colony formation of SDS patient BM. Finally, TGFβ3 and other TGFβ pathway members were elevated in SDS patient blood plasma. These data establish the TGFβ pathway as a novel candidate biomarker and therapeutic target in SDS and translate insights from single cell biology into a potential therapy.

Background Mathematical modeling of biological regulatory networks provides valuable insights into the structural and dynamical properties of the underlying systems. While dynamic models based on differential equations provide quantitative information on the biological systems, qualitative models that rely on the logical interactions among the components provide coarse-grained descriptions useful for systems whose mechanistic underpinnings remain incompletely understood. The middle ground class of piecewise affine differential equation models was proven informative for systems with partial knowledge of kinetic parameters. Methods In this work we provide a comparison of the dynamic characteristics of these three approaches applied on several biological regulatory network motifs. Specifically, we compare the attractors and state transitions in asynchronous Boolean, piecewise affine and Hill-type continuous models. Results Our study shows that while the fixed points of asynchronous Boolean models are observed in continuous Hill-type and piecewise affine models, these models may exhibit different attractors under certain conditions. Conclusions Overall, qualitative models are suitable for systems with limited knowledge of quantitative information. On the other hand, when practical, using quantitative models can provide detailed information about additional real-valued attractors not present in the qualitative models.

Shwachman-Diamond syndrome (SDS) is a rare and clinically heterogeneous bone marrow (BM) failure syndrome caused by mutations in the Shwachman-Bodian-Diamond syndrome (SBDS) gene. Although SDS was described more than 50 years ago, its molecular pathogenesis is poorly understood due, in part, to the rarity and heterogeneity of the affected hematopoietic progenitors. To address this, we used single-cell RNA sequencing to profile scant hematopoietic stem and progenitor cells from patients with SDS. We generated a single-cell map of early lineage commitment and found that SDS hematopoiesis was left-shifted with selective loss of granulocyte-monocyte progenitors. Transcriptional targets of transforming growth factor beta (TGF-[beta]) were dysregulated in SDS hematopoietic stem cells and multipotent progenitors, but not in lineage-committed progenitors. TGF-[beta] inhibitors (AVID200 and SD208) increased hematopoietic colony formation of SDS patient BM. Finally, TGF-[beta]3 and other TGF-[beta] pathway members were elevated in SDS patient blood plasma. These data establish the TGF-[beta] pathway as a candidate biomarker and therapeutic target in SDS and translate insights from single-cell biology into a potential therapy.

Recent advances have demonstrated the power of small molecules in promoting cellular reprogramming. Yet, the full potential of such chemicals in cell fate manipulation and the underlying mechanisms require further characterization. Through functional screening assays, we find that mouse embryonic fibroblast cells can be induced to trans-differentiate into a wide range of somatic lineages simultaneously by treatment with a combination of four chemicals. Genomic analysis of the process indicates activation of multi-lineage modules and relaxation of epigenetic silencing programs. In addition, we identify Sox2 as an important regulator within the induced network. Single cell analysis uncovers a novel priming state that enables transition from fibroblast cells to diverse somatic lineages. Finally, we demonstrate that modification of the culture system enables directional trans-differentiation towards myocytic, glial or adipocytic lineages. Our study describes a cell fate control system that may be harnessed for regenerative medicine.