Systems biology. Conditional density-based analysis of T cell signaling in single-cell data

MICA: a multi-omics method to predict gene regulatory networks in early human embryos

Recent advances in single-cell omics have transformed characterisation of cell types in challenging-to-study biological contexts. In contexts with limited single-cell samples, such as the early human embryo inference of transcription factor-gene regulatory network (GRN) interactions is especially difficult. Here, we assessed application of different linear or non-linear GRN predictions to single-cell simulated and human embryo transcriptome datasets. We also compared how expression normalisation impacts on GRN predictions, finding that transcripts per million reads outperformed alternative methods. GRN inferences were more reproducible using a non-linear method based on mutual information (MI) applied to single-cell transcriptome datasets refined with chromatin accessibility (CA) (called MICA), compared with alternative network prediction methods tested. MICA captures complex non-monotonic dependencies and feedback loops. Using MICA, we generated the first GRN inferences in early human development. MICA predicted co-localisation of the AP-1 transcription factor subunit proto-oncogene JUND and the TFAP2C transcription factor AP-2γ in early human embryos. Overall, our comparative analysis of GRN prediction methods defines a pipeline that can be applied to single-cell multi-omics datasets in especially challenging contexts to infer interactions between transcription factor expression and target gene regulation.

Inferring Metabolic States from Single Cell Transcriptomic Data via Geometric Deep Learning

The ability to measure gene expression at single-cell resolution has elevated our understanding of how biological features emerge from complex and interdependent networks at molecular, cellular, and tissue scales. As technologies have evolved that complement scRNAseq measurements with things like single-cell proteomic, epigenomic, and genomic information, it becomes increasingly apparent how much biology exists as a product of multimodal regulation. Biological processes such as transcription, translation, and post-translational or epigenetic modification impose both energetic and specific molecular demands on a cell and are therefore implicitly constrained by the metabolic state of the cell. While metabolomics is crucial for defining a holistic model of any biological process, the chemical heterogeneity of the metabolome makes it particularly difficult to measure, and technologies capable of doing this at single-cell resolution are far behind other multiomics modalities. To address these challenges, we present GEFMAP (Gene Expression-based Flux Mapping and Metabolic Pathway Prediction), a method based on geometric deep learning for predicting flux through reactions in a global metabolic network using transcriptomics data, which we ultimately apply to scRNAseq. GEFMAP leverages the natural graph structure of metabolic networks to learn both a biological objective for each cell and estimate a mass-balanced relative flux rate for each reaction in each cell using novel deep learning models.

Immune interactions in pembrolizumab (PD-1 inhibitor) cancer therapy and cardiovascular complications

The use of immunotherapies like pembrolizumab (PEM) is increasingly common for the management of numerous cancer types. The use of PEM to bolster T-cell response against tumor growth is well documented. However, the interactions PEM has on other immune cells to facilitate tumor regression and clearance is unknown and warrants further investigation. In this review, we present literature findings that have reported the interactions of PEM in stimulating innate and adaptive immune cells, which enhance cytotoxic phenotypes. This triggers secretion of cytokines and chemokines, which have both beneficial and detrimental effects. We also describe how this leads to the development of rare but underreported occurrence of PEM-induced immune-related cardiovascular complications that arise suddenly and progress rapidly to debilitating and fatal consequences. This review encourages further research and investigation of PEM-induced cardiovascular complications and other immune cell interactions in patients with cancer. As PEM therapy in treating cancer types is expanding, we expect that this review will inform health care professionals of diverse specializations of medicine like dermatology (melanoma skin cancers), ophthalmology (eye cancers), and pathology (hematological malignancies) about PEM-induced cardiac complications.

BioNetGMMFit: estimating parameters of a BioNetGen model from time-stamped snapshots of single cells

Mechanistic models are commonly employed to describe signaling and gene regulatory kinetics in single cells and cell populations. Recent advances in single-cell technologies have produced multidimensional datasets where snapshots of copy numbers (or abundances) of a large number of proteins and mRNA are measured across time in single cells. The availability of such datasets presents an attractive scenario where mechanistic models are validated against experiments, and estimated model parameters enable quantitative predictions of signaling or gene regulatory kinetics. To empower the systems biology community to easily estimate parameters accurately from multidimensional single-cell data, we have merged a widely used rule-based modeling software package BioNetGen, which provides a user-friendly way to code for mechanistic models describing biochemical reactions, and the recently introduced CyGMM, that uses cell-to-cell differences to improve parameter estimation for such networks, into a single software package: BioNetGMMFit. BioNetGMMFit provides parameter estimates of the model, supplied by the user in the BioNetGen markup language (BNGL), which yield the best fit for the observed single-cell, time-stamped data of cellular components. Furthermore, for more precise estimates, our software generates confidence intervals around each model parameter. BioNetGMMFit is capable of fitting datasets of increasing cell population sizes for any mechanistic model specified in the BioNetGen markup language. By streamlining the process of developing mechanistic models for large single-cell datasets, BioNetGMMFit provides an easily-accessible modeling framework designed for scale and the broader biochemical signaling community.

Advances in Clinical Mass Cytometry

The advent of high-dimensional single-cell technologies has enabled detection of cellular heterogeneity and functional diversity of immune cells during health and disease conditions. Because of its multiplexing capabilities and limited compensation requirements, mass cytometry or cytometry by time of flight (CyTOF) has played a superior role in immune monitoring compared with flow cytometry. Further, it has higher throughput and lower cost compared with other single-cell techniques. Several published articles have utilized CyTOF to identify cellular phenotypes and features associated with disease outcomes. This article introduces CyTOF-based assays to profile immune cell-types, cell-states, and their applications in clinical research.

Hybrid machine-learning framework for volumetric segmentation and quantification of vacuoles in individual yeast cells using holotomography

The precise, quantitative evaluation of intracellular organelles in three-dimensional (3D) imaging data poses a significant challenge due to the inherent constraints of traditional microscopy techniques, the requirements of the use of exogenous labeling agents, and existing computational methods. To counter these challenges, we present a hybrid machine-learning framework exploiting correlative imaging of 3D quantitative phase imaging with 3D fluorescence imaging of labeled cells. The algorithm, which synergistically integrates a random-forest classifier with a deep neural network, is trained using the correlative imaging data set, and the trained network is then applied to 3D quantitative phase imaging of cell data. We applied this method to live budding yeast cells. The results revealed precise segmentation of vacuoles inside individual yeast cells, and also provided quantitative evaluations of biophysical parameters, including volumes, concentration, and dry masses of automatically segmented vacuoles.

Computational Methods for Single-Cell Proteomics

Advances in single-cell proteomics technologies have resulted in high-dimensional datasets comprising millions of cells that are capable of answering key questions about biology and disease. The advent of these technologies has prompted the development of computational tools to process and visualize the complex data. In this review, we outline the steps of single-cell and spatial proteomics analysis pipelines. In addition to describing available methods, we highlight benchmarking studies that have identified advantages and pitfalls of the currently available computational toolkits. As these technologies continue to advance, robust analysis tools should be developed in tandem to take full advantage of the potential biological insights provided by these data.

A universal mass tag based on polystyrene nanoparticles for single-cell multiplexing with mass cytometry

Mass cytometry (MC) is an emerging bioanalytical technique for high-dimensional biomarkers interrogation simultaneously on individual cells. However, the sensitivity and multiplexed analysis ability of MC was highly restricted by the current metal chelating polymer (MCP) mass tags. Herein, a new design strategy for MC mass tags by using a commercial available and low cost classical material, polystyrene nanoparticle (PS-NP) to carry metals was reported. Unlike inorganic materials, sub-micron-grade metal-loaded polystyrene can be easily detected by MC, thus it is not essential to pursue extremely small particle size in this mass tag design strategy. An altered cell staining buffer can significantly lower the nonspecific binding (NSB) of non-functionalized PS-NPs, revealing another method to lower NSB beside surface modification. The metal doped PS-NP_Abs mass tags showed high compatibility with MCP mass tags and 5-fold higher sensitivity. By using Hf doped PS-NP_Abs as mass tags, four new MC detection channels (¹⁷⁷Hf, ¹⁷⁸Hf, ¹⁷⁹Hf and ¹⁸⁰Hf) were developed. In general, this work provides a new strategy in designing MC mass tags and lowering NSB, opening up possibility of introducing more potential MC mass tag candidates.

Single-Cell Proteomics with Spatial Attributes: Tools and Techniques

Now-a-days, the single-cell proteomics (SCP) concept is attracting interest, especially in clinical research, because it can identify the proteomic signature specific to diseased cells. This information is very essential when dealing with the progression of certain diseases, such as cancer, diabetes, Alzheimer's, etc. One of the major drawbacks of conventional destructive proteomics is that it gives an average idea about the protein expression profile in the disease condition. During the extraction of the protein from a biopsy or blood sample, proteins may come from both diseased cells and adjacent normal cells or any other cells from the disease environment. Again, SCP along with spatial attributes is utilized to learn about the heterogeneous function of a single protein. Before performing SCP, it is necessary to isolate single cells. This can be done by various techniques, including fluorescence-activated cell sorting (FACS), magnetic-activated cell sorting (MACS), laser capture microdissection (LCM), microfluidics, manual cell picking/micromanipulation, etc. Among the different approaches for proteomics, mass spectrometry-based proteomics tools are widely used for their high resolution as well as sensitivity. This Review mainly focuses on the mass spectrometry-based approaches for the study of single-cell proteomics.

Single-cell analysis reveals inflammatory interactions driving macular degeneration

Due to commonalities in pathophysiology, age-related macular degeneration (AMD) represents a uniquely accessible model to investigate therapies for neurodegenerative diseases, leading us to examine whether pathways of disease progression are shared across neurodegenerative conditions. Here we use single-nucleus RNA sequencing to profile lesions from 11 postmortem human retinas with age-related macular degeneration and 6 control retinas with no history of retinal disease. We create a machine-learning pipeline based on recent advances in data geometry and topology and identify activated glial populations enriched in the early phase of disease. Examining single-cell data from Alzheimer's disease and progressive multiple sclerosis with our pipeline, we find a similar glial activation profile enriched in the early phase of these neurodegenerative diseases. In late-stage age-related macular degeneration, we identify a microglia-to-astrocyte signaling axis mediated by interleukin-1β which drives angiogenesis characteristic of disease pathogenesis. We validated this mechanism using in vitro and in vivo assays in mouse, identifying a possible new therapeutic target for AMD and possibly other neurodegenerative conditions. Thus, due to shared glial states, the retina provides a potential system for investigating therapeutic approaches in neurodegenerative diseases.

Pleiotropic role of TRAF7 in skull-base meningiomas and congenital heart disease

While somatic variants of TRAF7 (Tumor necrosis factor receptor-associated factor 7) underlie anterior skull-base meningiomas, here we report the inherited mutations of TRAF7 that cause congenital heart defects. We show that TRAF7 mutants operate in a dominant manner, inhibiting protein function via heterodimerization with wild-type protein. Further, the shared genetics of the two disparate pathologies can be traced to the common origin of forebrain meninges and cardiac outflow tract from the TRAF7-expressing neural crest. Somatic and inherited mutations disrupt TRAF7-IFT57 interactions leading to cilia degradation. TRAF7-mutant meningioma primary cultures lack cilia, and TRAF7 knockdown causes cardiac, craniofacial, and ciliary defects in Xenopus and zebrafish, suggesting a mechanistic convergence for TRAF7-driven meningiomas and developmental heart defects.

Identifying strengths and weaknesses of methods for computational network inference from single-cell RNA-seq data

Single-cell RNA-sequencing (scRNA-seq) offers unparalleled insight into the transcriptional programs of different cellular states by measuring the transcriptome of thousands of individual cells. An emerging problem in the analysis of scRNA-seq is the inference of transcriptional gene regulatory networks and a number of methods with different learning frameworks have been developed to address this problem. Here, we present an expanded benchmarking study of eleven recent network inference methods on seven published scRNA-seq datasets in human, mouse, and yeast considering different types of gold standard networks and evaluation metrics. We evaluate methods based on their computing requirements as well as on their ability to recover the network structure. We find that, while most methods have a modest recovery of experimentally derived interactions based on global metrics such as Area Under the Precision Recall curve, methods are able to capture targets of regulators that are relevant to the system under study. Among the top performing methods that use only expression were SCENIC, PIDC, MERLIN or Correlation. Addition of prior biological knowledge and the estimation of transcription factor activities resulted in the best overall performance with the Inferelator and MERLIN methods that use prior knowledge outperforming methods that use expression alone. We found that imputation for network inference did not improve network inference accuracy and could be detrimental. Comparisons of inferred networks for comparable bulk conditions showed that the networks inferred from scRNA-seq datasets are often better or at par with the networks inferred from bulk datasets. Our analysis should be beneficial in selecting methods for network inference. At the same time, this highlights the need for improved methods and better gold standards for regulatory network inference from scRNAseq datasets.

The race to understand immunopathology in COVID-19: Perspectives on the impact of quantitative approaches to understand within-host interactions

The COVID-19 pandemic has revealed the need for the increased integration of modelling and data analysis to public health, experimental, and clinical studies. Throughout the first two years of the pandemic, there has been a concerted effort to improve our understanding of the within-host immune response to the SARS-CoV-2 virus to provide better predictions of COVID-19 severity, treatment and vaccine development questions, and insights into viral evolution and the impacts of variants on immunopathology. Here we provide perspectives on what has been accomplished using quantitative methods, including predictive modelling, population genetics, machine learning, and dimensionality reduction techniques, in the first 26 months of the COVID-19 pandemic approaches, and where we go from here to improve our responses to this and future pandemics.

Quantitative Imaging Analysis of NF-κB for Mathematical Modeling Applications

Mathematical models can integrate different types of experimental datasets, reconstitute biological systems in silico, and identify previously unknown molecular mechanisms. Over the past decade, mathematical models have been developed based on quantitative observations, such as live-cell imaging and biochemical assays. However, it is difficult to directly integrate next-generation sequencing (NGS) data. Although highly dimensional, NGS data mostly only provides a "snapshot" of cellular states. Nevertheless, the development of various methods for NGS analysis has led to much more accurate predictions of transcription factor activity and has revealed various concepts regarding transcriptional regulation. Therefore, fluorescence live-cell imaging of transcription factors can help alleviate the limitations in NGS data by supplementing temporal information, linking NGS to mathematical modeling. This chapter introduces an analytical method for quantifying dynamics of nuclear factor kappaB (NF-κB) which forms aggregates in the nucleus. The method may also be applicable to other transcription factors regulated in a similar fashion.

The role of iron nanoparticles on morpho-physiological traits and genes expression (IRT1 and CAT) in rue (Ruta graveolens)

The iron nanoparticles with different physic-chemical properties induce inconsistent effects on various studied plant species. Thus, the effect of ferric oxide (Fe₂O₃) nanoparticles was compared with Fe₂O₃ microparticles and FeSO₄complexes of EDTA for major physiological and gene expression in Rue (Ruta graveolens). Iron root content increased as Fe-MPs + EDTA ˂˂ Fe-NPs + EDTA˂ FeSO₄ + EDTA. The shoot's iron remained unchanged or slightly increased under most of FeSO₄ and Fe-MPs + EDTA treatments. Under Fe-NPs + EDTA treatment, 50 and 250 µM concentration decreased on shoot iron by 23.2% and 19.4% compared to control, respectively. But the shoot iron at 500 µM NPs was 28.2% higher than that of the control. A 46-58 fold lower Fe translocation was observed under Fe-NPs + EDTA than Fe-MPs + EDTA. The effect of Fe-NPs + EDTA was more significant on plant fresh and dry mass than the control. All treatments showed an increase in anthocyanin by 19-84% in leaves compared to the control. The Fe-NPs + EDTA and MPs + EDTA induced similar effects on enhanced growth parameters, total chlorophyll, catalase enzyme activity, gene, and reduced chlorophyll a/b and oxidants. Catalase enzyme activity in FeSO₄ and MPs + EDTA was similar, and in Fe-NPs + EDTA treatments were influenced by coarse and fine regulation mechanisms, respectively. Iron MPs + EDTA had a more negative effect on IRT₁ relative gene expression in roots as compared to other iron forms. The IRT₁ relative gene expression in shoots was positively affected by 31-81% under all treatment types (except control and 250 µM Fe-NPs + EDTA, and 250 µM MPs + EDTA). These results could reveal the potential mechanism of plant response to nanoparticles.

Quantifying information of intracellular signaling: progress with machine learning

Cells convey information about their extracellular environment to their core functional machineries. Studying the capacity of intracellular signaling pathways to transmit information addresses fundamental questions about living systems. Here, we review how information-theoretic approaches have been used to quantify information transmission by signaling pathways that are functionally pleiotropic and subject to molecular stochasticity. We describe how recent advances in machine learning have been leveraged to address the challenges of complex temporal trajectory datasets and how these have contributed to our understanding of how cells employ temporal coding to appropriately adapt to environmental perturbations.

Bi-order multimodal integration of single-cell data

Integration of single-cell multiomics profiles generated by different single-cell technologies from the same biological sample is still challenging. Previous approaches based on shared features have only provided approximate solutions. Here, we present a novel mathematical solution named bi-order canonical correlation analysis (bi-CCA), which extends the widely used CCA approach to iteratively align the rows and the columns between data matrices. Bi-CCA is generally applicable to combinations of any two single-cell modalities. Validations using co-assayed ground truth data and application to a CAR-NK study and a fetal muscle atlas demonstrate its capability in generating accurate multimodal co-embeddings and discovering cellular identity.

Multiscale PHATE identifies multimodal signatures of COVID-19

As the biomedical community produces datasets that are increasingly complex and high dimensional, there is a need for more sophisticated computational tools to extract biological insights. We present Multiscale PHATE, a method that sweeps through all levels of data granularity to learn abstracted biological features directly predictive of disease outcome. Built on a coarse-graining process called diffusion condensation, Multiscale PHATE learns a data topology that can be analyzed at coarse resolutions for high-level summarizations of data and at fine resolutions for detailed representations of subsets. We apply Multiscale PHATE to a coronavirus disease 2019 (COVID-19) dataset with 54 million cells from 168 hospitalized patients and find that patients who die show CD16hiCD66blo neutrophil and IFN-γ+ granzyme B+ Th17 cell responses. We also show that population groupings from Multiscale PHATE directly fed into a classifier predict disease outcome more accurately than naive featurizations of the data. Multiscale PHATE is broadly generalizable to different data types, including flow cytometry, single-cell RNA sequencing (scRNA-seq), single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq), and clinical variables.

The Intriguing Landscape of Single-Cell Protein Analysis

Profiling protein expression at single-cell resolution is essential for fundamental biological research (such as cell differentiation and tumor microenvironmental examination) and clinical precision medicine where only a limited number of primary cells are permitted. With the recent advances in engineering, chemistry, and biology, single-cell protein analysis methods are developed rapidly, which enable high-throughput and multiplexed protein measurements in thousands of individual cells. In combination with single cell RNA sequencing and mass spectrometry, single-cell multi-omics analysis can simultaneously measure multiple modalities including mRNAs, proteins, and metabolites in single cells, and obtain a more comprehensive exploration of cellular signaling processes, such as DNA modifications, chromatin accessibility, protein abundance, and gene perturbation. Here, the recent progress and applications of single-cell protein analysis technologies in the last decade are summarized. Current limitations, challenges, and possible future directions in this field are also discussed.

IQCELL: A platform for predicting the effect of gene perturbations on developmental trajectories using single-cell RNA-seq data

The increasing availability of single-cell RNA-sequencing (scRNA-seq) data from various developmental systems provides the opportunity to infer gene regulatory networks (GRNs) directly from data. Herein we describe IQCELL, a platform to infer, simulate, and study executable logical GRNs directly from scRNA-seq data. Such executable GRNs allow simulation of fundamental hypotheses governing developmental programs and help accelerate the design of strategies to control stem cell fate. We first describe the architecture of IQCELL. Next, we apply IQCELL to scRNA-seq datasets from early mouse T-cell and red blood cell development, and show that the platform can infer overall over 74% of causal gene interactions previously reported from decades of research. We will also show that dynamic simulations of the generated GRN qualitatively recapitulate the effects of known gene perturbations. Finally, we implement an IQCELL gene selection pipeline that allows us to identify candidate genes, without prior knowledge. We demonstrate that GRN simulations based on the inferred set yield results similar to the original curated lists. In summary, the IQCELL platform offers a versatile tool to infer, simulate, and study executable GRNs in dynamic biological systems.

Executable GRNs provide an important strategy to model complex intracellular dynamics in development and disease. Here we introduce IQCELL, a platform to infer, simulate, and study executable logical GRNs directly from single cell sequencing data. IQCELL is an integrative platform that includes modules for gene selection, building logical GRNs, and simulating developmental trajectories under normal and perturbed conditions. We demonstrate the utility of IQCELL by reconstructing GRNs for early mouse T-cell and red blood cell development. We show that IQCELL can “automatically” infer the vast majority of gene interactions previously reported from decades of experimental research. IQCELL also provides users with a platform to simulate the developmental trajectories of cells. We show that dynamic simulations of the inferred GRNs resemble experimentally observed gene expression dynamics and capture the effects of genetic perturbation studies. IQCELL offers a versatile tool to infer and simulate GRNs in dynamic biological systems.

Revealing new biology from multiplexed, metal-isotope-tagged, single-cell readouts

Mass cytometry (MC) is a recent technology that pairs plasma-based ionization of cells in suspension with time-of-flight (TOF) mass spectrometry to sensitively quantify the single-cell abundance of metal-isotope-tagged affinity reagents to key proteins, RNA, and peptides. Given the ability to multiplex readouts (~50 per cell) and capture millions of cells per experiment, MC offers a robust way to assay rare, transitional cell states that are pertinent to human development and disease. Here, we review MC approaches that let us probe the dynamics of cellular regulation across multiple conditions and sample types in a single experiment. Additionally, we discuss current limitations and future extensions of MC as well as computational tools commonly used to extract biological insight from single-cell proteomic datasets.

Analysis and Visualization of Spatial Transcriptomic Data

Human and animal tissues consist of heterogeneous cell types that organize and interact in highly structured manners. Bulk and single-cell sequencing technologies remove cells from their original microenvironments, resulting in a loss of spatial information. Spatial transcriptomics is a recent technological innovation that measures transcriptomic information while preserving spatial information. Spatial transcriptomic data can be generated in several ways. RNA molecules are measured by in situ sequencing, in situ hybridization, or spatial barcoding to recover original spatial coordinates. The inclusion of spatial information expands the range of possibilities for analysis and visualization, and spurred the development of numerous novel methods. In this review, we summarize the core concepts of spatial genomics technology and provide a comprehensive review of current analysis and visualization methods for spatial transcriptomics.

Deep learning tackles single-cell analysis-a survey of deep learning for scRNA-seq analysis

Since its selection as the method of the year in 2013, single-cell technologies have become mature enough to provide answers to complex research questions. With the growth of single-cell profiling technologies, there has also been a significant increase in data collected from single-cell profilings, resulting in computational challenges to process these massive and complicated datasets. To address these challenges, deep learning (DL) is positioned as a competitive alternative for single-cell analyses besides the traditional machine learning approaches. Here, we survey a total of 25 DL algorithms and their applicability for a specific step in the single cell RNA-seq processing pipeline. Specifically, we establish a unified mathematical representation of variational autoencoder, autoencoder, generative adversarial network and supervised DL models, compare the training strategies and loss functions for these models, and relate the loss functions of these models to specific objectives of the data processing step. Such a presentation will allow readers to choose suitable algorithms for their particular objective at each step in the pipeline. We envision that this survey will serve as an important information portal for learning the application of DL for scRNA-seq analysis and inspire innovative uses of DL to address a broader range of new challenges in emerging multi-omics and spatial single-cell sequencing.

Modeling uniquely human gene regulatory function via targeted humanization of the mouse genome

The evolution of uniquely human traits likely entailed changes in developmental gene regulation. Human Accelerated Regions (HARs), which include transcriptional enhancers harboring a significant excess of human-specific sequence changes, are leading candidates for driving gene regulatory modifications in human development. However, insight into whether HARs alter the level, distribution, and timing of endogenous gene expression remains limited. We examined the role of the HAR HACNS1 (HAR2) in human evolution by interrogating its molecular functions in a genetically humanized mouse model. We find that HACNS1 maintains its human-specific enhancer activity in the mouse embryo and modifies expression of Gbx2, which encodes a transcription factor, during limb development. Using single-cell RNA-sequencing, we demonstrate that Gbx2 is upregulated in the limb chondrogenic mesenchyme of HACNS1 homozygous embryos, supporting that HACNS1 alters gene expression in cell types involved in skeletal patterning. Our findings illustrate that humanized mouse models provide mechanistic insight into how HARs modified gene expression in human evolution.

Multiplexed single-cell analysis of organoid signaling networks

Organoids are biomimetic tissue models comprising multiple cell types and cell states. Post-translational modification (PTM) signaling networks control cellular phenotypes and are frequently dysregulated in diseases such as cancer. Although signaling networks vary across cell types, there are limited techniques to study cell type-specific PTMs in heterocellular organoids. Here, we present a multiplexed mass cytometry (MC) protocol for single-cell analysis of PTM signaling and cell states in organoids and organoids co-cultured with fibroblasts and leukocytes. We describe how thiol-reactive organoid barcoding in situ (TOBis) enables 35-plex and 126-plex single-cell comparison of organoid cultures and provide a cytometry by time of flight (CyTOF) signaling analysis pipeline (CyGNAL) for computing cell type-specific PTM signaling networks. The TOBis MC protocol takes ~3 d from organoid fixation to data acquisition and can generate single-cell data for >40 antibodies from millions of cells across 126 organoid cultures in a single MC run.

Cell-to-cell and type-to-type heterogeneity of signaling networks: insights from the crowd

Recent technological developments allow us to measure the status of dozens of proteins in individual cells. This opens the way to understand the heterogeneity of complex multi-signaling networks across cells and cell types, with important implications to understand and treat diseases such as cancer. These technologies are, however, limited to proteins for which antibodies are available and are fairly costly, making predictions of new markers and of existing markers under new conditions a valuable alternative. To assess our capacity to make such predictions and boost further methodological development, we organized the Single Cell Signaling in Breast Cancer DREAM challenge. We used a mass cytometry dataset, covering 36 markers in over 4,000 conditions totaling 80 million single cells across 67 breast cancer cell lines. Through four increasingly difficult subchallenges, the participants predicted missing markers, new conditions, and the time-course response of single cells to stimuli in the presence and absence of kinase inhibitors. The challenge results show that despite the stochastic nature of signal transduction in single cells, the signaling events are tightly controlled and machine learning methods can accurately predict new experimental data.

Single-cell analysis of prostaglandin E2-induced human decidual cell in vitro differentiation: a minimal ancestral deciduogenic signal†

The decidua is a hallmark of reproduction in many placental mammals. Differentiation of decidual stromal cells is known to be induced by progesterone and the cyclic AMP/protein kinase A (cAMP/PKA) pathway. Several candidates have been identified as the physiological stimulus for adenylyl cyclase activation, but their relative importance remains unclear. To bypass this uncertainty, the standard approach for in vitro experiments uses membrane-permeable cAMP and progestin. We phylogenetically infer that prostaglandin E2 (PGE2) likely was the signal that ancestrally induced decidualization in conjunction with progesterone. This suggests that PGE2 and progestin should be able to activate the core gene regulatory network of decidual cells. To test this prediction, we performed a genome-wide study of gene expression in human endometrial fibroblasts decidualized with PGE2 and progestin. Comparison to a cAMP-based protocol revealed shared activation of core decidual genes and decreased induction of senescence-associated genes. Single-cell transcriptomics of PGE2-mediated decidualization revealed a distinct, early-activated state transitioning to a differentiated decidual state. PGE2-mediated decidualization was found to depend upon progestin-dependent induction of PGE2 receptor 2 (PTGER2) which in turn leads to PKA activation upon PGE2 stimulation. Progesterone-dependent induction of PTGER2 is absent in opossum, an outgroup taxon of placental mammals which is incapable of decidualization. Together, these findings suggest that the origin of decidualization involved the evolution of progesterone-dependent activation of the PGE2/PTGER2/PKA axis, facilitating entry into a PKA-dominant rather than AKT-dominant cellular state. We propose the use of PGE2 for in vitro decidualization as an alternative to 8-Br-cAMP.

Single-cell secretion analysis reveals a dual role for IL-10 in restraining and resolving the TLR4-induced inflammatory response

Following Toll-like receptor 4 (TLR4) stimulation of macrophages, negative feedback mediated by the anti-inflammatory cytokine interleukin-10 (IL-10) limits the inflammatory response. However, extensive cell-to-cell variability in TLR4-stimulated cytokine secretion raises questions about how negative feedback is robustly implemented. To explore this, we characterize the TLR4-stimulated secretion program in primary murine macrophages using a single-cell microwell assay that enables evaluation of functional autocrine IL-10 signaling. High-dimensional analysis of single-cell data reveals three tiers of TLR4-induced proinflammatory activation based on levels of cytokine secretion. Surprisingly, while IL-10 inhibits TLR4-induced activation in the highest tier, it also contributes to the TLR4-induced activation threshold by regulating which cells transition from non-secreting to secreting states. This role for IL-10 in restraining TLR4 inflammatory activation is largely mediated by intermediate interferon (IFN)-β signaling, while TNF likely mediates response resolution by IL-10. Thus, cell-to-cell variability in cytokine regulatory motifs provides a means to tailor the TLR4-induced inflammatory response.

New Structure Mass Tag based on Zr-NMOF for Multiparameter and Sensitive Single-Cell Interrogating in Mass Cytometry

Mass cytometry, also called cytometry by time-of-flight (CyTOF), is an emerging powerful proteomic analysis technique that utilizes metal chelated polymer (MCP) as mass tags for interrogating high-dimensional biomarkers simultaneously on millions of individual cells. However, under the typical polymer-based mass tag system, the sensitivity and multiplexing detection ability has been highly restricted. Herein, a new structure mass tag based on a nanometal organic framework (NMOF) is reported for multiparameter and sensitive single-cell biomarker interrogating in CyTOF. A uniform-sized Zr-NMOF (33 nm) carrying 10⁵ metal ions is synthesized under modulator/reaction time coregulation, which is monodispersed and colloidally stable in water for over one-year storage. On functionalization with an antibody, the Zr mass tag exhibits specific molecular recognition properties and minimal cross-reaction toward nontargeted cells. In addition, the Zr-mass tag is compatible with MCP mass tags in a multiparameter assay for mouse spleen cells staining, which exploits four additional channels, m/z = 90, 91, 92, 94, for single-cell immunoassays in CyTOF. Compared to the MCP mass tag, the Zr-mass tag provides an additional fivefold signal amplification. This work provides the fundamental technical capability for exploiting NMOF-based mass tags for CyTOF application, which opens up possibility of high-dimensional single-cell immune profiling, low abundant antigen detection, and development of new barcoding systems.

Towards high throughput and high information coverage: advanced single-cell mass spectrometric techniques

Mass spectrometry (MS) is attractive for single-cell analysis because of its high sensitivity, rich information, and large dynamic ranges, especially for the single-cell metabolome and proteome analysis. Efforts have been made to deal with the throughput and information coverage problems in typical manual single-cell MS techniques. In this review, advanced techniques to improve the automation and throughput for single-cell sampling and single-cell metabolome and proteome MS detection have been discussed. Furthermore, representative MS-based strategies that can increase the in-depth cellular information coverage and achieve the more comprehensive single-cell multiomics information during high throughput detection have been highlighted, providing an ongoing perspective of the MS performance for the single-cell research.

DIAPH1 Variants in Non-East Asian Patients With Sporadic Moyamoya Disease

Importance

Moyamoya disease (MMD), a progressive vasculopathy leading to narrowing and ultimate occlusion of the intracranial internal carotid arteries, is a cause of childhood stroke. The cause of MMD is poorly understood, but genetic factors play a role. Several familial forms of MMD have been identified, but the cause of most cases remains elusive, especially among non-East Asian individuals.

Objective

To assess whether ultrarare de novo and rare, damaging transmitted variants with large effect sizes are associated with MMD risk.

Design, Setting, and Participants

A genetic association study was conducted using whole-exome sequencing case-parent MMD trios in a small discovery cohort collected over 3.5 years (2016-2019); data were analyzed in 2020. Medical records from US hospitals spanning a range of 1 month to 1.5 years were reviewed for phenotyping. Exomes from a larger validation cohort were analyzed to identify additional rare, large-effect variants in the top candidate gene. Participants included patients with MMD and, when available, their parents. All participants who met criteria and were presented with the option to join the study agreed to do so; none were excluded. Twenty-four probands (22 trios and 2 singletons) composed the discovery cohort, and 84 probands (29 trios and 55 singletons) composed the validation cohort.

Main Outcomes and Measures

Gene variants were identified and filtered using stringent criteria. Enrichment and case-control tests assessed gene-level variant burden. In silico modeling estimated the probability of variant association with protein structure. Integrative genomics assessed expression patterns of MMD risk genes derived from single-cell RNA sequencing data of human and mouse brain tissue.

Results

Of the 24 patients in the discovery cohort, 14 (58.3%) were men and 18 (75.0%) were of European ancestry. Three of 24 discovery cohort probands contained 2 do novo (1-tailed Poisson P = 1.1 × 10-6) and 1 rare, transmitted damaging variant (12.5% of cases) in DIAPH1 (mammalian diaphanous-1), a key regulator of actin remodeling in vascular cells and platelets. Four additional ultrarare damaging heterozygous DIAPH1 variants (3 unphased) were identified in 3 other patients in an 84-proband validation cohort (73.8% female, 77.4% European). All 6 patients were non-East Asian. Compound heterozygous variants were identified in ena/vasodilator-stimulated phosphoproteinlike protein EVL, a mammalian diaphanous-1 interactor that regulates actin polymerization. DIAPH1 and EVL mutant probands had severe, bilateral MMD associated with transfusion-dependent thrombocytopenia. DIAPH1 and other MMD risk genes are enriched in mural cells of midgestational human brain. The DIAPH1 coexpression network converges in vascular cell actin cytoskeleton regulatory pathways.

Conclusions and Relevance

These findings provide the largest collection to date of non-East Asian individuals with sporadic MMD harboring pathogenic variants in the same gene. The results suggest that DIAPH1 is a novel MMD risk gene and impaired vascular cell actin remodeling in MMD pathogenesis, with diagnostic and therapeutic ramifications.

Single-cell analysis by mass cytometry reveals metabolic states of early-activated CD8+ T cells during the primary immune response

Memory T cells are thought to rely on oxidative phosphorylation and short-lived effector T cells on glycolysis. Here, we investigated how T cells arrive at these states during an immune response. To understand the metabolic state of rare, early-activated T cells, we adapted mass cytometry to quantify metabolic regulators at single-cell resolution in parallel with cell signaling, proliferation, and effector function. We interrogated CD8+ T cell activation in vitro and in response to Listeria monocytogenes infection in vivo. This approach revealed a distinct metabolic state in early-activated T cells characterized by maximal expression of glycolytic and oxidative metabolic proteins. Cells in this transient state were most abundant 5 days post-infection before rapidly decreasing metabolic protein expression. Analogous findings were observed in chimeric antigen receptor (CAR) T cells interrogated longitudinally in advanced lymphoma patients. Our study demonstrates the utility of single-cell metabolic analysis by mass cytometry to identify metabolic adaptations of immune cell populations in vivo and provides a resource for investigations of metabolic regulation of immune responses across a variety of applications.

Landscapes of cellular phenotypic diversity in breast cancer xenografts and their impact on drug response

The heterogeneity of breast cancer plays a major role in drug response and resistance and has been extensively characterized at the genomic level. Here, a single-cell breast cancer mass cytometry (BCMC) panel is optimized to identify cell phenotypes and their oncogenic signalling states in a biobank of patient-derived tumour xenograft (PDTX) models representing the diversity of human breast cancer. The BCMC panel identifies 13 cellular phenotypes (11 human and 2 murine), associated with both breast cancer subtypes and specific genomic features. Pre-treatment cellular phenotypic composition is a determinant of response to anticancer therapies. Single-cell profiling also reveals drug-induced cellular phenotypic dynamics, unravelling previously unnoticed intra-tumour response diversity. The comprehensive view of the landscapes of cellular phenotypic heterogeneity in PDTXs uncovered by the BCMC panel, which is mirrored in primary human tumours, has profound implications for understanding and predicting therapy response and resistance.

Single-Cell Proteomics

The inability to make broad, minimally biased measurements of a cell's proteome stands as a major bottleneck for understanding how gene expression translates into cellular phenotype. Unlike sequencing for nucleic acids, there is no dominant method for making single-cell proteomic measurements. Instead, methods typically focus on either absolute quantification of a small number of proteins or highly multiplexed protein measurements. Advances in microfluidics and output encoding have led to major improvements in both aspects. Here, we review the most recent progress that has enabled hundreds of protein measurements and ultrahigh-sensitivity quantification. We also highlight emerging technologies such as single-cell mass spectrometry that may enable unbiased measurement of cellular proteomes.

Immune-stimulating antibody conjugates elicit robust myeloid activation and durable antitumor immunity

Innate pattern recognition receptor agonists, including Toll-like receptors (TLRs), alter the tumor microenvironment and prime adaptive antitumor immunity. However, TLR agonists present toxicities associated with widespread immune activation after systemic administration. To design a TLR-based therapeutic suitable for systemic delivery and capable of safely eliciting tumor-targeted responses, we developed immune-stimulating antibody conjugates (ISACs) comprising a TLR7/8 dual agonist conjugated to tumor-targeting antibodies. Systemically administered human epidermal growth factor receptor 2 (HER2)-targeted ISACs were well tolerated and triggered a localized immune response in the tumor microenvironment that resulted in tumor clearance and immunological memory. Mechanistically, ISACs required tumor antigen recognition, Fcγ-receptor-dependent phagocytosis and TLR-mediated activation to drive tumor killing by myeloid cells and subsequent T-cell-mediated antitumor immunity. ISAC-mediated immunological memory was not limited to the HER2 ISAC target antigen since ISAC-treated mice were protected from rechallenge with the HER2- parental tumor. These results provide a strong rationale for the clinical development of ISACs.

Inferring signaling pathways with probabilistic programming

Motivation

Cells regulate themselves via dizzyingly complex biochemical processes called signaling pathways. These are usually depicted as a network, where nodes represent proteins and edges indicate their influence on each other. In order to understand diseases and therapies at the cellular level, it is crucial to have an accurate understanding of the signaling pathways at work. Since signaling pathways can be modified by disease, the ability to infer signaling pathways from condition- or patient-specific data is highly valuable. A variety of techniques exist for inferring signaling pathways. We build on past works that formulate signaling pathway inference as a Dynamic Bayesian Network structure estimation problem on phosphoproteomic time course data. We take a Bayesian approach, using Markov Chain Monte Carlo to estimate a posterior distribution over possible Dynamic Bayesian Network structures. Our primary contributions are (i) a novel proposal distribution that efficiently samples sparse graphs and (ii) the relaxation of common restrictive modeling assumptions.

Results

We implement our method, named Sparse Signaling Pathway Sampling, in Julia using the Gen probabilistic programming language. Probabilistic programming is a powerful methodology for building statistical models. The resulting code is modular, extensible and legible. The Gen language, in particular, allows us to customize our inference procedure for biological graphs and ensure efficient sampling. We evaluate our algorithm on simulated data and the HPN-DREAM pathway reconstruction challenge, comparing our performance against a variety of baseline methods. Our results demonstrate the vast potential for probabilistic programming, and Gen specifically, for biological network inference.

Availability and implementation

Find the full codebase at https://github.com/gitter-lab/ssps.

Supplementary information

Supplementary data are available at Bioinformatics online.

Transcriptomic and clonal characterization of T cells in the human central nervous system

T cells provide critical immune surveillance to the central nervous system (CNS), and the cerebrospinal fluid (CSF) is thought to be a main route for their entry. Further characterization of the state of T cells in the CSF in healthy individuals is important for understanding how T cells provide protective immune surveillance without damaging the delicate environment of the CNS and providing tissue-specific context for understanding immune dysfunction in neuroinflammatory disease. Here, we have profiled T cells in the CSF of healthy human donors and have identified signatures related to cytotoxic capacity and tissue adaptation that are further exemplified in clonally expanded CSF T cells. By comparing profiles of clonally expanded T cells obtained from the CSF of patients with multiple sclerosis (MS) and healthy donors, we report that clonally expanded T cells from the CSF of patients with MS have heightened expression of genes related to T cell activation and cytotoxicity.

The prospects of single-cell analysis in autoimmunity

In the last decade, there has been a tremendous development of technologies focused on analysing various molecular attributes in single cells, with an ever-increasing number of parameters becoming available at the DNA, RNA and protein levels. Much of this progress has involved cells in suspension, but also in situ analysis of tissues has taken great leaps. Paralleling the development in the laboratory, and because of increasing complexity, the analysis of single-cell data is also constantly being updated with new algorithms and analysis platforms. Our immune system shares this complexity, and immunologists have therefore been in the forefront of this technological development. These technologies clearly open new avenues for immunology research, maybe particularly within autoimmunity where the interaction between the faulty immune system and the thymus or the target organ is important. However, the technologies currently available can seem overwhelming and daunting. The aim of this review is to remedy this by giving a balanced overview of the prospects of using single-cell analysis in basal and clinical autoimmunity research, with an emphasis on endocrine autoimmunity.

The winning methods for predicting cellular position in the DREAM single-cell transcriptomics challenge

MOTIVATION

Predicting cell locations is important since with the understanding of cell locations, we may estimate the function of cells and their integration with the spatial environment. Thus, the DREAM challenge on single-cell transcriptomics required participants to predict the locations of single cells in the Drosophila embryo using single-cell transcriptomic data.

RESULTS

We have developed over 50 pipelines by combining different ways of preprocessing the RNA-seq data, selecting the genes, predicting the cell locations and validating predicted cell locations, resulting in the winning methods which were ranked second in sub-challenge 1, first in sub-challenge 2 and third in sub-challenge 3. In this paper, we present an R package, SCTCwhatateam, which includes all the methods we developed and the Shiny web application to facilitate the research on single-cell spatial reconstruction. All the data and the example use cases are available in the Supplementary data.

Network Approaches for Dissecting the Immune System

The immune system is a complex biological network composed of hierarchically organized genes, proteins, and cellular components that combat external pathogens and monitor the onset of internal disease. To meet and ultimately defeat these challenges, the immune system orchestrates an exquisitely complex interplay of numerous cells, often with highly specialized functions, in a tissue-specific manner. One of the major methodologies of systems immunology is to measure quantitatively the components and interaction levels in the immunologic networks to construct a computational network and predict the response of the components to perturbations. The recent advances in high-throughput sequencing techniques have provided us with a powerful approach to dissecting the complexity of the immune system. Here we summarize the latest progress in integrating omics data and network approaches to construct networks and to infer the underlying signaling and transcriptional landscape, as well as cell-cell communication, in the immune system, with a focus on hematopoiesis, adaptive immunity, and tumor immunology. Understanding the network regulation of immune cells has provided new insights into immune homeostasis and disease, with important therapeutic implications for inflammation, cancer, and other immune-mediated disorders.

The Importance of Computational Modeling in Stem Cell Research

The generation of large amounts of omics data is increasingly enabling not only the processing and analysis of large data sets but also the development of computational models in the field of stem cell research. Although computational models have been proposed in recent decades, we believe that the stem cell community is not fully aware of the potentiality of computational modeling in guiding their experimental research. In this regard, we discuss how single-cell technologies provide the right framework for computational modeling at different scales of biological organization in order to address challenges in the stem cell field and to guide experimentalists in the design of new strategies for stem cell therapies and treatment of congenital disorders.

Single-cell proteomic analysis

The ability to comprehensively profile proteins in every individual cell of complex biological systems is crucial to advance our understanding of normal physiology and disease pathogenesis. Conventional bulk cell experiments mask the cell heterogeneity in the population, while the single-cell imaging methods suffer from the limited multiplexing capacities. Recent advances in microchip-, mass spectrometry-, and reiterative staining-based technologies have enabled comprehensive protein profiling in single cells. These approaches will bring new insights into a variety of biological and biomedical fields, such as signaling network regulation, cell heterogeneity, tissue architecture, disease diagnosis, and treatment monitoring. In this article, we will review the recent advances in the development of single-cell proteomic technologies, describe their advantages, discuss the current limitations and challenges, and propose potential solutions. We will also highlight the wide applications of these technologies in biology and medicine. This article is categorized under: Cancer > Molecular and Cellular Physiology.

IL-7 receptor alpha defines heterogeneity and signature of human effector memory CD8+ T cells in high dimensional analysis

The IL-7 receptor alpha chain (IL-7Rα or CD127) can be differentially expressed in memory CD8⁺ T cells. Here we investigated whether IL-7Rα could serve as a key molecule in defining a comprehensive landscape of heterogeneity in human effector memory (EM) CD8⁺ T cells using high-dimensional Cytometry by Time-Of-Flight (CyTOF) and single-cell RNA-seq (scRNA-seq). IL-7Rα had diverse, but organized, expressional relationship in EM CD8⁺ T cells with molecules related to cell function and gene regulation, which rendered an immune landscape defining heterogeneous cell subsets. The differential expression of these molecules likely has biological implications as we found in vivo signatures of transcription factors and homeostasis cytokine receptors, including T-bet and IL-7Rα. Our findings indicate the existence of heterogeneity in human EM CD8⁺ T cells as defined by distinct but organized expression patterns of multiple molecules in relationship to IL-7Rα and its possible biological significance in modulating downstream events.

Understanding the Dynamics of T-Cell Activation in Health and Disease Through the Lens of Computational Modeling

T cells in the immune system are activated by binding to foreign peptides (from an external pathogen) or mutant peptide (derived from endogenous proteins) displayed on the surface of a diseased cell. This triggers a series of intracellular signaling pathways, which ultimately dictate the response of the T cell. The insights from computational models have greatly improved our understanding of the mechanisms that control T-cell activation. In this review, we focus on the use of ordinary differential equation–based mechanistic models to study T-cell activation. We highlight several examples that demonstrate the models’ utility in answering specific questions related to T-cell activation signaling, from antigen discrimination to the feedback mechanisms that initiate transcription factor activation. In addition, we describe other modeling approaches that can be combined with mechanistic models to bridge time scales and better understand how intracellular signaling events, which occur on the order of seconds to minutes, influence phenotypic responses of T-cell activation, which occur on the order of hours to days. Overall, through concrete examples, we emphasize how computational modeling can be used to enable the rational design and optimization of immunotherapies.

Stimulation strength controls the rate of initiation but not the molecular organisation of TCR-induced signalling

Millions of naïve T cells with different TCRs may interact with a peptide-MHC ligand, but very few will activate. Remarkably, this fine control is orchestrated using a limited set of intracellular machinery. It remains unclear whether changes in stimulation strength alter the programme of signalling events leading to T cell activation. Using mass cytometry to simultaneously measure multiple signalling pathways during activation of murine CD8+ T cells, we found a programme of distal signalling events that is shared, regardless of the strength of TCR stimulation. Moreover, the relationship between transcription of early response genes Nr4a1 and Irf8 and activation of the ribosomal protein S6 is also conserved across stimuli. Instead, we found that stimulation strength dictates the rate with which cells initiate signalling through this network. These data suggest that TCR-induced signalling results in a coordinated activation program, modulated in rate but not organization by stimulation strength.

Ultra-high throughput single-cell analysis of proteins and RNAs by split-pool synthesis

Single-cell omics provide insight into cellular heterogeneity and function. Recent technological advances have accelerated single-cell analyses, but workflows remain expensive and complex. We present a method enabling simultaneous, ultra-high throughput single-cell barcoding of millions of cells for targeted analysis of proteins and RNAs. Quantum barcoding (QBC) avoids isolation of single cells by building cell-specific oligo barcodes dynamically within each cell. With minimal instrumentation (four 96-well plates and a multichannel pipette), cell-specific codes are added to each tagged molecule within cells through sequential rounds of classical split-pool synthesis. Here we show the utility of this technology in mouse and human model systems for as many as 50 antibodies to targeted proteins and, separately, >70 targeted RNA regions. We demonstrate that this method can be applied to multi-modal protein and RNA analyses. It can be scaled by expansion of the split-pool process and effectively renders sequencing instruments as versatile multi-parameter flow cytometers.

Maeve O’Huallachain et al. report a method that enables simultaneous, ultra-high throughput single-cell barcoding for targeted single-cell protein and RNA analysis. They show the utility of their method in analyses of mRNA and protein expression in human and mouse cells.

Profiling Cell Signaling Networks at Single-cell Resolution

Signaling networks process intra- and extracellular information to modulate the functions of a cell. Deregulation of signaling networks results in abnormal cellular physiological states and often drives diseases. Network responses to a stimulus or a drug treatment can be highly heterogeneous across cells in a tissue because of many sources of cellular genetic and non-genetic variance. Signaling network heterogeneity is the key to many biological processes, such as cell differentiation and drug resistance. Only recently, the emergence of multiplexed single-cell measurement technologies has made it possible to evaluate this heterogeneity. In this review, we categorize currently established single-cell signaling network profiling approaches by their methodology, coverage, and application, and we discuss the advantages and limitations of each type of technology. We also describe the available computational tools for network characterization using single-cell data and discuss potential confounding factors that need to be considered in single-cell signaling network analyses.

Single-Cell Proteomic Profiling Identifies Combined AXL and JAK1 Inhibition as a Novel Therapeutic Strategy for Lung Cancer

Cytometry by time-of-flight (CyTOF) simultaneously measures multiple cellular proteins at the single-cell level and is used to assess intertumor and intratumor heterogeneity. This approach may be used to investigate the variability of individual tumor responses to treatments. Herein, we stratified lung tumor subpopulations based on AXL signaling as a potential targeting strategy. Integrative transcriptome analyses were used to investigate how TP-0903, an AXL kinase inhibitor, influences redundant oncogenic pathways in metastatic lung cancer cells. CyTOF profiling revealed that AXL inhibition suppressed SMAD4/TGFβ signaling and induced JAK1-STAT3 signaling to compensate for the loss of AXL. Interestingly, high JAK1-STAT3 was associated with increased levels of AXL in treatment-naïve tumors. Tumors with high AXL, TGFβ, and JAK1 signaling concomitantly displayed CD133-mediated cancer stemness and hybrid epithelial-to-mesenchymal transition features in advanced-stage patients, suggesting greater potential for distant dissemination. Diffusion pseudotime analysis revealed cell-fate trajectories among four different categories that were linked to clinicopathologic features for each patient. Patient-derived organoids (PDO) obtained from tumors with high AXL and JAK1 were sensitive to TP-0903 and ruxolitinib (JAK inhibitor) treatments, supporting the CyTOF findings. This study shows that single-cell proteomic profiling of treatment-naïve lung tumors, coupled with ex vivo testing of PDOs, identifies continuous AXL, TGFβ, and JAK1-STAT3 signal activation in select tumors that may be targeted by combined AXL-JAK1 inhibition. SIGNIFICANCE: Single-cell proteomic profiling of clinical samples may facilitate the optimal selection of novel drug targets, interpretation of early-phase clinical trial data, and development of predictive biomarkers valuable for patient stratification.