Multiple Cell States Research Articles

A de novo Missense Mutation in PPP2R5D Alters Dopamine Pathways and Morphology of iPSC-derived Midbrain Neurons.

Induced pluripotent stem cell (iPSC) models of neurodevelopmental disorders (NDDs) have promoted an understanding of commonalities and differences within or across patient populations by revealing the underlying molecular and cellular mechanisms contributing to disease pathology. Here, we focus on developing a human model for PPP2R5D-related NDD, called Jordan syndrome, which has been linked to Early-Onset Parkinson's Disease (EOPD). Here we sought to understand the underlying molecular and cellular phenotypes across multiple cell states and neuronal subtypes in order to gain insight into Jordan syndrome pathology. Our work revealed that iPSC-derived midbrain neurons from Jordan syndrome patients display significant differences in dopamine-associated pathways and neuronal architecture. We then evaluated a CRISPR-based approach for editing heterozygous dominant G-to-A mutations at the transcript level in patient-derived neural stem cells. Our findings show site-directed RNA editing is influenced by sgRNA length and cell type. These studies support the potential for a CRISPR RNA editor system to selectively edit mutant transcripts harboring G-to-A mutations in neural stem cells while providing an alternative editing technology for those suffering from NDDs.

A mathematical framework for understanding the spontaneous emergence of complexity applicable to growing multicellular systems.

In embryonic development and organogenesis, cells sharing identical genetic codes acquire diverse gene expression states in a highly reproducible spatial distribution, crucial for multicellular formation and quantifiable through positional information. To understand the spontaneous growth of complexity, we constructed a one-dimensional division-decision model, simulating the growth of cells with identical genetic networks from a single cell. Our findings highlight the pivotal role of cell division in providing positional cues, escorting the system toward states rich in information. Moreover, we pinpointed lateral inhibition as a critical mechanism translating spatial contacts into gene expression. Our model demonstrates that the spatial arrangement resulting from cell division, combined with cell lineages, imparts positional information, specifying multiple cell states with increased complexity-illustrated through examples in C.elegans. This study constitutes a foundational step in comprehending developmental intricacies, paving the way for future quantitative formulations to construct synthetic multicellular patterns.

Spatial transition tensor of single cells

Spatial transcriptomics and messenger RNA splicing encode extensive spatiotemporal information for cell states and transitions. The current lineage-inference methods either lack spatial dynamics for state transition or cannot capture different dynamics associated with multiple cell states and transition paths. Here we present spatial transition tensor (STT), a method that uses messenger RNA splicing and spatial transcriptomes through a multiscale dynamical model to characterize multistability in space. By learning a four-dimensional transition tensor and spatial-constrained random walk, STT reconstructs cell-state-specific dynamics and spatial state transitions via both short-time local tensor streamlines between cells and long-time transition paths among attractors. Benchmarking and applications of STT on several transcriptome datasets via multiple technologies on epithelial–mesenchymal transitions, blood development, spatially resolved mouse brain and chicken heart development, indicate STT’s capability in recovering cell-state-specific dynamics and their associated genes not seen using existing methods. Overall, STT provides a consistent multiscale description of single-cell transcriptome data across multiple spatiotemporal scales.

Abstract 934: RNA-based precision medicine predicts sensitivity to selinexor in select pancreatic ductal adenocarcinoma patients

Abstract Sensitivity to therapeutic agents is impacted both by genetic heterogeneity between patients or among clones within a tumor and by heterogeneity of epigenetic cell states. Prior work demonstrates that human pancreatic tumors comprise malignant cells in a mixture of multiple cell states with distinct genetic dependencies, leading to a reservoir of chemoresistance across the tumor; targeting just one or two malignant cell states is insufficient to achieve durable therapeutic responses. One approach to overcome this challenge is to identify agents with efficacy against malignant cells in multiple cell states. However, in the absence of genetic distinctions between cell states, identifying therapeutic vulnerabilities for each malignant cell state present in a patient’s tumor in real time remains a challenge. OncoTreat is a CLIA certified algorithm that matches drugs to patients based on tumor gene expression profiles. The approach utilizes regulatory network analysis, a systems biology framework that transforms gene expression data into regulatory protein activity profiles, in a manner that facilitates the identification of master transcriptional regulators of cell state. We carried out a Phase 1 clinical trial of the OncoTreat framework to examine feasibility for implementing RNA-based precision medicine in patients with metastatic pancreatic ductal adenocarcinoma (2nd line and beyond). Patient-derived xenografts were established from subjects prior to 1st line therapy, and selected agents matched by OncoTreat were assessed co-clinically while subjects received standard of care regimens, with the potential for subsequent treatment with successful agents upon progression. Two subjects in the study matched to selenexor, an XPO1 inhibitor that is FDA approved for multiple myeloma. Co-clinical studies of selinexor in patient-derived xenograft models generated from the matched subjects showed prolonged survival compared to control regimens. Single-cell RNA sequencing analysis of these tumors found that the activity of master regulators confirmed that the activity of selinexor-responsive master regulators were inverted in response to treatment in vivo, reducing the fraction of malignant cells in the tumors. Strikingly, selenexor treatment was associated with significant inhibition of RAS/MAPK signaling, suggesting a potential novel role for selinexor targeting Ras. Together, these data indicate a potential therapeutic vulnerability for a subset of PDAC patients that can be predicted by a CLIA-certified RNA-based precision medicine platform. Citation Format: Alvaro Curiel-Garcia, Lorenzo Tomassoni, Tanner C. Dalton, Amanda R. Decker-Farrell, Carmine F. Palermo, Daniel R. Ross, Stephen A. Sastra, Urszula N. Wasko, Isabel M. Goncalves, Prabhot Mundi, Basil S. Bakir, Rachael A. Safyan, Hanina Hibshoosh, Gulam A. Manji, Andrea Califano, Kenneth P. Olive. RNA-based precision medicine predicts sensitivity to selinexor in select pancreatic ductal adenocarcinoma patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 934.

Abstract 6940: The transcriptional and epigenetic regulation of epithelial-mesenchymal plasticity in patient-derived pancreatic cancer cell lines

Abstract Epithelial-mesenchymal transition (EMT) is a hallmark of pancreatic ductal adenocarcinoma (PDAC) invasion. In general, cellular plasticity is a major contributor to both tumor progression and therapy resistance. The major goals of our study are to 1) directly and quantitatively measure plasticity in PDAC3 cell lines, and 2) identify candidate genes that could alter the plasticity of cancer cells. By understanding the regulation of tumor cell plasticity, we may find ways to perturb cells and prevent the transition towards treatment resistant cell states. A strict definition of plasticity in cancer has yet to be established, however, and is imperative to our understanding of cell state regulation and for identifying vulnerabilities in plastic cells. We argue that in order to prove plasticity occurs within a population of cells, one must either 1) demonstrate that a new cell state has emerged that was previously not present, or 2) trace the lineage of a cell to show it can adopt multiple cell states. For our study, we profile 12 patient-derived PDAC cell lines to find convergent EMT programs and perform lineage tracing on these cell lines using a modified CROP-seq vector, called ClonMapper, to quantitatively measure plasticity. Towards this goal, we perform single-cell RNA-seq (scRNA) and single-cell multiomic measurements of 1000 uniquely barcoded cells expanded over a time course of 2, 3 and 4 weeks. We refer to cells that have the same lineage barcode - originating from the same initial cell - as families. We found that ~50% of families are composed of cells that are epithelial and mesenchymal, thus proving plasticity must exist, as it shows that a given parental cell of unknown state can give rise to progeny that are in multiple states. We then develop mathematical models to show that families have different levels of plasticity, defined by the rates of transition between distinct cell states. We next leverage our barcode-resolution time course data to identify factors at early time points that may influence plasticity at later time points. We show there is differential accessibility of several of these factors across families with different plasticity properties, thus proposing a role for epigenetic regulation of EMT. Importantly, we find that specific factors drive gene regulatory networks important for epithelial and mesenchymal programs, and therefore propose candidates for the modulation of EMT for therapeutic benefit. Citation Format: Deepika Yeramosu, Lynn Bi, Abigail Collins, Mike Bogaev, Martin Jankowiak, Aziz Al’Khafaji, Milan Parikh, Mehrtash Babadi, Alex Bloemendal, Surya Nagaraja, Ray Jones, Jay Shendure, David T. Ting, Andrew Aguirre, Nir Hacohen, Dana Pe’er, Eric S. Lander, Arnav Mehta. The transcriptional and epigenetic regulation of epithelial-mesenchymal plasticity in patient-derived pancreatic cancer cell lines [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6940.

Long-term super-resolution inner mitochondrial membrane imaging with a lipid probe

The inner mitochondrial membrane (IMM) generates power to drive cell function, and its dynamics control mitochondrial health and cellular homeostasis. Here, we describe the cell-permeant, lipid-like small molecule MAO-N3 and use it to assemble high-density environmentally sensitive (HIDE) probes that selectively label and image the IMM in live cells and multiple cell states. MAO-N3 pairs with strain-promoted azide–alkyne click chemistry-reactive fluorophores to support HIDE imaging using confocal, structured illumination, single-molecule localization and stimulated emission depletion microscopy, all with significantly improved resistance to photobleaching. These probes generate images with excellent spatial and temporal resolution, require no genetic manipulations, are non-toxic in model cell lines and primary cardiomyocytes (even under conditions that amplify the effects of mitochondrial toxins) and can visualize mitochondrial dynamics for 12.5 h. This probe will enable comprehensive studies of IMM dynamics with high temporal and spatial resolution.

Single-cell mass cytometry analysis reveals stem cell heterogeneity.

Cellular heterogeneity is fundamental to both developmental differentiation and disease establishment. Recent advances in high-throughput single-cell technology have been rapidly revolutionizing the resolution of our understanding of development and disease. However, while the study of single-cell transcriptomes is easily accessible, the analysis of single-cell proteomes is still in its infancy. In this study, we describe simultaneous profiling of multiple regulatory proteins at a single-cell level using mass cytometry or cytometry by time of flight. We develop mass cytometry reagents to study key transcription factors, signaling proteins and chromatin modifiers that regulate mouse embryonic stem cells. Our data reveal that the protein level of stem cell regulators significantly varies and that cell signaling pathways are extensively cross-activated across defined culture conditions of embryonic stem cells. In addition, the mass cytometry data enabled us to identify distinct multiple cell states of embryonic stem cells and determine their variation across culture conditions. We discuss the mass cytometry method, our results of the multi-protein analysis in embryonic stem cells and potential future perspectives for single-cell protein analysis.

Computational Model of Heterogeneity in Melanoma: Designing Therapies and Predicting Outcomes.

Cutaneous melanoma is a highly invasive tumor and, despite the development of recent therapies, most patients with advanced metastatic melanoma have a poor clinical outcome. The most frequent mutations in melanoma affect the BRAF oncogene, a protein kinase of the MAPK signaling pathway. Therapies targeting both BRAF and MEK are effective for only 50% of patients and, almost systematically, generate drug resistance. Genetic and non-genetic mechanisms associated with the strong heterogeneity and plasticity of melanoma cells have been suggested to favor drug resistance but are still poorly understood. Recently, we have introduced a novel mathematical formalism allowing the representation of the relation between tumor heterogeneity and drug resistance and proposed several models for the development of resistance of melanoma treated with BRAF/MEK inhibitors. In this paper, we further investigate this relationship by using a new computational model that copes with multiple cell states identified by single cell mRNA sequencing data in melanoma treated with BRAF/MEK inhibitors. We use this model to predict the outcome of different therapeutic strategies. The reference therapy, referred to as “continuous” consists in applying one or several drugs without disruption. In “combination therapy”, several drugs are used sequentially. In “adaptive therapy” drug application is interrupted when the tumor size is below a lower threshold and resumed when the size goes over an upper threshold. We show that, counter-intuitively, the optimal protocol in combination therapy of BRAF/MEK inhibitors with a hypothetical drug targeting cell states that develop later during the tumor response to kinase inhibitors, is to treat first with this hypothetical drug. Also, even though there is little difference in the timing of emergence of the resistance between continuous and adaptive therapies, the spatial distribution of the different melanoma subpopulations is more zonated in the case of adaptive therapy.

Evaluation of Cell Viability with a Single Fluorescent Probe Based on Two Kinds of Fluorescence Signal Modes

Accurate evaluation of cell viability is important for dosage tests of anticancer drugs, pathology, and numerous biological experiments. However, due to the serious insufficieny of fluorescent probes, which can distinguish various cell states, the study of cell viability is immensely limited. To resolve this issue, we design and synthesize a new probe ACD-E to monitor cell viability with two kinds of fluorescence signal modes, the first single fluorescent probe that can distinguish three different cell states and furnish accurate information in biological experiments. ACD-E can discriminate live and dead cells in a dual-color mode by evaluating cell mitochondrial esterase activity and can also discriminate live and early necrosis cells by determining mitochondrial viscosity in a "turn-on" mode in the near-infrared region. Significantly, the novel ACD-E can also distinguish cell viability in vivo. This work establishes a robust strategy for monitoring multiple cell states using a single fluorescent probe.

Interpretable, Scalable, and Transferrable Functional Projection of Large-Scale Transcriptome Data Using Constrained Matrix Decomposition.

Large-scale transcriptome data, such as single-cell RNA-sequencing data, have provided unprecedented resources for studying biological processes at the systems level. Numerous dimensionality reduction methods have been developed to visualize and analyze these transcriptome data. In addition, several existing methods allow inference of functional variations among samples using gene sets with known biological functions. However, it remains challenging to analyze transcriptomes with reduced dimensions that are interpretable in terms of dimensions’ directionalities, transferrable to new data, and directly expose the contribution or association of individual genes. In this study, we used gene set non-negative principal component analysis (gsPCA) and non-negative matrix factorization (gsNMF) to analyze large-scale transcriptome datasets. We found that these methods provide low-dimensional information about the progression of biological processes in a quantitative manner, and their performances are comparable to existing functional variation analysis methods in terms of distinguishing multiple cell states and samples from multiple conditions. Remarkably, upon training with a subset of data, these methods allow predictions of locations in the functional space using data from experimental conditions that are not exposed to the models. Specifically, our models predicted the extent of progression and reversion for cells in the epithelial-mesenchymal transition (EMT) continuum. These methods revealed conserved EMT program among multiple types of single cells and tumor samples. Finally, we demonstrate this approach is broadly applicable to data and gene sets beyond EMT and provide several recommendations on the choice between the two linear methods and the optimal algorithmic parameters. Our methods show that simple constrained matrix decomposition can produce to low-dimensional information in functionally interpretable and transferrable space, and can be widely useful for analyzing large-scale transcriptome data.

SECAT: Quantifying Protein Complex Dynamics across Cell States by Network-Centric Analysis of SEC-SWATH-MS Profiles.

Protein-protein interactions (PPIs) play critical functional and regulatory roles in cellular processes. They are essential for macromolecular complex formation, which in turn constitutes the basis for protein interaction networks that determine the functional state of a cell. We and others have previously shown that chromatographic fractionation of native protein complexes in combination with bottom-up mass spectrometric analysis of consecutive fractions supports the multiplexed characterization and detection of state-specific changes of protein complexes. In this study, we extend co-fractionation and mass spectrometric data analysis to perform quantitative, network-based studies of proteome organization, via the size-exclusion chromatography algorithmic toolkit (SECAT). This framework explicitly accounts for the dynamic nature and rewiring of protein complexes across multiple cell states and samples, thus, elucidating molecular mechanisms that are differentially implemented across different experimental settings. Systematic analysis of multiple datasets shows that SECAT represents a highly scalable and effective methodology to assess condition/state-specific protein-network state. A record of this paper's transparent peer review process is included in the Supplemental Information.

Hippo pathway deletion in adult resting cardiac fibroblasts initiates a cell state transition with spontaneous and self-sustaining fibrosis.

Cardiac fibroblasts (CFs) respond to injury by transitioning through multiple cell states, including resting CFs, activated CFs, and myofibroblasts. We report here that Hippo signaling cell-autonomously regulates CF fate transitions and proliferation, and non-cell-autonomously regulates both myeloid and CF activation in the heart. Conditional deletion of Hippo pathway kinases, Lats1 and Lats2, in uninjured CFs initiated a self-perpetuating fibrotic response in the adult heart that was exacerbated by myocardial infarction (MI). Single cell transcriptomics showed that uninjured Lats1/2 mutant CFs spontaneously transitioned to a myofibroblast cell state. Through gene regulatory network reconstruction, we found that Hippo-deficient myofibroblasts deployed a network of transcriptional regulators of endoplasmic reticulum (ER) stress, and the unfolded protein response (UPR) consistent with elevated secretory activity. We observed an expansion of myeloid cell heterogeneity in uninjured Lats1/2 CKO hearts with similarity to cells recovered from control hearts post-MI. Integrated genome-wide analysis of Yap chromatin occupancy revealed that Yap directly activates myofibroblast cell identity genes, the proto-oncogene Myc, and an array of genes encoding pro-inflammatory factors through enhancer-promoter looping. Our data indicate that Lats1/2 maintain the resting CF cell state through restricting the Yap-induced injury response.

CoExpresso: assess the quantitative behavior of protein complexes in human cells

BackgroundTranslational and post-translational control mechanisms in the cell result in widely observable differences between measured gene transcription and protein abundances. Herein, protein complexes are among the most tightly controlled entities by selective degradation of their individual proteins. They furthermore act as control hubs that regulate highly important processes in the cell and exhibit a high functional diversity due to their ability to change their composition and their structure. Better understanding and prediction of these functional states demands methods for the characterization of complex composition, behavior, and abundance across multiple cell states. Mass spectrometry provides an unbiased approach to directly determine protein abundances across different cell populations and thus to profile a comprehensive abundance map of proteins.ResultsWe provide a tool to investigate the behavior of protein subunits in known complexes by comparing their abundance profiles across up to 140 cell types available in ProteomicsDB. Thorough assessment of different randomization methods and statistical scoring algorithms allows determining the significance of concurrent profiles within a complex, therefore providing insights into the conservation of their composition across human cell types as well as the identification of intrinsic structures in complex behavior to determine which proteins orchestrate complex function. This analysis can be extended to investigate common profiles within arbitrary protein groups. CoExpresso can be accessed through http://computproteomics.bmb.sdu.dk/Apps/CoExpresso.ConclusionsWith the CoExpresso web service, we offer a potent scoring scheme to assess proteins for their co-regulation and thereby offer insight into their potential for forming functional groups like protein complexes.

Experimental and statistical methods to evaluate antibacterial activity of a quaternary pyridinium salt on planktonic, biofilm-forming, and biofilm states

Robust evaluation and comparison of antimicrobial technologies are critical to improving biofilm prevention and treatment. Herein, a multi-pronged experimental framework and statistical models were applied to determine the effects of quaternary pyridinium salt, 4-acetyl-1-hexadecylpyridin-1-ium iodide (QPS-1), on Streptococcus mutans in the planktonic, biofilm-forming and biofilm cell states. Minimum inhibitory and bactericidal concentrations (MIC and MBC, respectively) were determined via common methods with novel application of statistical approaches combining random effects models and interval censored data to estimate uncertainties. The MICs and MBCs for planktonic and biofilm-forming states ranged from 3.12 to 12.5 μg ml−1, with biofilm values only ≈ 8 times higher. Potent anti-biofilm activity and reactive structural features make QPS-1 a promising antibacterial additive for dental and potentially other biomedical devices. Together, the experimental framework and statistical models provide estimates and uncertainties for effective antimicrobial concentrations in multiple cell states, enabling statistical comparisons and improved characterization of antibacterial agents.

Highlights from Recent Cancer Literature

Highlights from Recent Cancer Literature

Plasmodium falciparum: Genome wide perturbations in transcript profiles among mixed stage cultures after chloroquine treatment

A genomic approach was taken to study the effect of chloroquine (CQ) on Plasmodium falciparum cultures in multiple cell states, following short and long exposures to drug at varying concentrations. Six hundred genes from numerous functional groups were responsive to CQ amongst all cell states assayed in a micro-array analysis; however, the amplitude of fold-change was low in the majority of cases. Moreover, alterations in specific, functionally related cascades could not be discerned, leading us to believe there is no single signature response to CQ at the transcript level in P. falciparum. Instead, cell cycle changes appear to have a more pronounced effect on gene expression; only a fraction of the drug responsive loci (∼5%) were shared between two separate starting cultures that varied in staging profile in the current study, as well as a previous published analysis using SAGE technology [Gunasekera, A.M., Patankar, S., Schug, J., Eisen, G.,Wirth, D.F., 2003. Drug-induced alterations in gene expression of the asexual blood forms of Plasmodium falciparum. Molecular Microbiology 50, 1229–1239]. These findings are important to report, given the striking contrast to similar studies in other model eukaryotic organisms.

Cancer onset and progression: A genome-wide, nonlinear dynamical systems perspective on onconetworks

It is hypothesized that the many human cell types corresponding to multiple states is supported by an underlying nonlinear dynamical system (NDS) of transcriptional regulatory network (TRN) processes. This hypothesis is validated for epithelial cells whose TRN is found to support an extremely complex array of states that we term a “bifurcation nexus”, for which we introduce a quantitative measure of complexity. The TRN used is constructed and analyzed by integrating a database of TRN information, cDNA microarray data analyzers, bioinformatics modules, a transcription/translation/post-translation kinetic model, and NDS analysis software. Results of this genome-wide approach suggest that a cell can be induced to persist in one state or to transition between distinct states; apparently irreversible transitions can be reversed when the high dimensional space of extracellular and intracellular parameters is understood. As conditions change, certain cellular states (cell lines) are no longer supported, new ones emerge, and transitions (cell differentiation or death) occur. The accumulation of simulated point mutations (minor changes which individually are insignificant) lead to occasional dramatic transitions. The genome-wide scope of many of these transitions is shown to arise from the cross-linked TRN structure. These notions imply that studying individual oncogenes may not be sufficient to understand cancer; rather, “onconetworks” (subsets of strongly coupled genes supporting multiple cell states) should be considered. Our approach reveals several epithelial onconetworks, each involving oncogenes and anti-tumor and supporting genes.