Non-genetic origins of cell-to-cell variability in TRAIL-induced apoptosis

Multivalent assembly of nucleolin-targeted F3 peptide potentiates TRAIL's tumor penetration and antitumor effects

Tumor-targeting drug delivery holds great promise for cancer treatment but faces significant challenges in penetrating solid tumors to achieve optimal therapeutic efficacy. By harnessing the natural tissue-penetration effect conferred by the CendR motif, we identified that the nucleolin (NCL)-targeted peptide F3 possesses tumor-penetrating capabilities. Co-administration of F3 with doxorubicin and the apoptosis-inducing protein TRAIL enhanced effective tumor penetration and improved antitumor activity. Taking advantage of TRAIL's natural self-trimerization, we developed a novel fusion protein, F3-TRAIL. This design enabled the trivalent assembly of F3 when fused with TRAIL, significantly enhancing its binding to NCL-positive tumor endothelial and parenchymal cells, resulting in deeper tumor penetration and superior antitumor effects compared to TRAIL alone. Mechanistic studies revealed that the multivalent F3-enhanced engagement with tumor cells potentiated TRAIL to trigger death receptor-dependent apoptosis signaling, even in TRAIL-resistant tumor cells. Building on this success, we constructed F3-HexaTR using the SpyCatcher/SpyTag superglue ligation system to generate a hexameric TRAIL, further amplifying cytotoxicity and antitumor efficacy. Combined analysis of data from TCGA and GTEx revealed significantly elevated NCL expression across 18 solid tumor types, underscoring the clinical potential of F3-directed targeted therapy. These findings highlight that F3-mediated NCL targeting is an effective strategy to overcome tumor penetration barriers, particularly for protein drug delivery. This multivalent assembly approach represents an innovative avenue for enhancing the therapeutic efficacy of various agents in the treatment of solid tumors.

Understanding therapeutic tolerance through a mathematical model of drug-induced resistance

There is growing recognition that phenotypic plasticity enables cancer cells to adapt to various environmental conditions. An example of this adaptability is the ability of an initially sensitive population of cancer cells to acquire resistance and persist in the presence of therapeutic agents. Understanding the implications of this drug-induced resistance is essential for predicting transient and long-term tumor dynamics subject to treatment. This paper introduces a mathematical model of drug-induced resistance which provides excellent fits to time-resolved in vitro experimental data. From observational data of total numbers of cells, the model unravels the relative proportions of sensitive and resistance subpopulations and quantifies their dynamics as a function of drug dose. The predictions are then validated using data on drug doses that were not used when fitting parameters. Optimal control techniques are then utilized to discover dosing strategies that could lead to better outcomes as quantified by lower total cell volume.

Single-cell analyses reveal increased gene expression variability in human neurodevelopmental conditions

Interindividual variation in phenotypic penetrance and severity is found in many neurodevelopmental conditions, although the underlying mechanisms remain largely unresolved. Within individuals, homogeneous cell types (i.e., genetically identical and in similar environments) can differ in molecule abundance. Here, we investigate the hypothesis that neurodevelopmental conditions can drive increased variability in gene expression, not just differential gene expression. Leveraging independent single-cell and single-nucleus RNA sequencing datasets derived from human brain-relevant cell and tissue types, we identify a significant increase in gene expression variability driven by the autosomal aneuploidy trisomy 21 (T21) as well as autism-associated chromodomain helicase DNA binding protein 8 (CHD8) haploinsufficiency. Our analyses are consistent with a global and, in part, stochastic increase in variability, which is uncoupled from changes in transcript abundance. Highly variable genes tend to be cell-type specific with modest enrichment for repressive H3K27me3, while least variable genes are more likely to be constrained and associated with active histone marks. Our results indicate that human neurodevelopmental conditions can drive increased gene expression variability in brain cell types, with the potential to contribute to diverse phenotypic outcomes. These findings also provide a scaffold for understanding variability in disease, essential for deeper insights into genotype-phenotype relationships.

Gradient matching accelerates mixed-effects inference for biochemical networks

MOTIVATION

Single-cell time series data often exhibit significant variability within an isogenic cell population. When modeling intracellular processes, it is therefore more appropriate to infer parameter distributions that reflect this variability, rather than fitting the population average to obtain a single point estimate. The Global Two-Stage (GTS) approach for nonlinear mixed-effects (NLME) models is a simple and modular method commonly used for this purpose. However, this method is computationally intensive due to its repeated use of nonconvex optimization and numerical integration of the underlying system.

RESULTS

We propose the Gradient Matching GTS (GMGTS) method as an efficient alternative to GTS. Gradient matching offers an integration-free approach to parameter estimation that is particularly powerful for systems that are linear in the unknown parameters, such as biochemical networks modeled by mass action kinetics. By incorporating gradient matching into the GTS framework, we expand its capabilities through uncertainty propagation calculations and an iterative estimation scheme for partially observed systems. Comparisons between GMGTS and GTS across various inference setups show that our method significantly reduces computational demands, facilitating the application of complex NLME models in systems biology.

AVAILABILITY AND IMPLEMENTATION

A Matlab implementation of GMGTS is provided at https://github.com/yulanvanoppen/GMGTS (DOI: http://doi.org/10.5281/zenodo.14884457).

Multi-step genomics on single cells and live cultures in sub-nanoliter capsules

Single-cell genomics encompasses a set of methods whereby hundreds to millions of cells are individually subjected to multiplexed assays including sequencing DNA, chromatin accessibility or modification, RNA, or combinations thereof1,2. These methods enable unbiased, systematic discovery of cellular phenotypes and their dynamics1-3. Many functional genomic methods, however, require multiple steps that cannot be easily scaled to high throughput, including assays on living cells. Here we develop capsules with amphiphilic gel envelopes (CAGEs), which selectively retain cells, mRNA, and gDNA, while allowing free diffusion of media, enzymes and reagents. CAGEs enable carrying out high-throughput assays that require multiple steps, including combining genomics with live-cell assays. We establish methods for barcoding CAGE DNA and RNA libraries, and apply them to measure persistence of gene expression programs by capturing the transcriptomes of tens of thousands of expanding clones in CAGEs. The compatibility of CAGEs with diverse enzymatic reactions will facilitate the expansion of the current repertoire of single-cell, high-throughput measurements and extend them to live-cell assays.

An integrative phenotype-structured partial differential equation model for the population dynamics of epithelial-mesenchymal transition

Phenotypic heterogeneity along the epithelial-mesenchymal (E-M) axis contributes to cancer metastasis and drug resistance. Recent experimental efforts have collated detailed time-course data on the emergence and dynamics of E-M heterogeneity in a cell population. However, it remains unclear how different intra- and inter-cellular processes shape the dynamics of E-M heterogeneity. Here, using Cell Population Balance model, we capture the dynamics of cell density along E-M phenotypic axis resulting from interplay between-(a) intracellular regulatory interaction among biomolecules, (b) cell division and death and (c) stochastic cell-state transition. We find that while the existence of E-M heterogeneity depends on intracellular regulation, heterogeneity gets enhanced with stochastic cell-state transitions and diminished by growth rate differences. Further, resource competition among E-M cells can lead to both bi-phasic growth of the total population and/or bi-stability in the phenotypic composition. Overall, our model highlights complex interplay between cellular processes shaping dynamic patterns of E-M heterogeneity.

Deep learning-based image classification reveals heterogeneous execution of cell death fates during viral infection

Cell fate decisions, such as proliferation, differentiation, and death, are driven by complex molecular interactions and signaling cascades. While significant progress has been made in understanding the molecular determinants of these processes, historically, cell fate transitions were identified through light microscopy that focused on changes in cell morphology and function. Modern techniques have shifted toward probing molecular effectors to quantify these transitions, offering more precise quantification and mechanistic understanding. However, challenges remain in cases where the molecular signals are ambiguous, complicating the assignment of cell fate. During viral infection, programmed cell death (PCD) pathways, including apoptosis, necroptosis, and pyroptosis, exhibit complex signaling and molecular cross-talk. This can lead to simultaneous activation of multiple PCD pathways, which confounds assignment of cell fate based on molecular information alone. To address this challenge, we employed deep learning-based image classification of dying cells to analyze PCD in single herpes simplex virus-1 (HSV-1)-infected cells. Our approach reveals that despite heterogeneous activation of signaling, individual cells adopt predominantly prototypical death morphologies. Nevertheless, PCD is executed heterogeneously within a uniform population of virus-infected cells and varies over time. These findings demonstrate that image-based phenotyping can provide valuable insights into cell fate decisions, complementing molecular assays.

A low level of tumor necrosis factor α in tumor microenvironment maintains the self-renewal of glioma stem cells by Vasorin-mediated glycolysis

BACKGROUND

Self-renewal of glioma stem cells (GSCs) is responsible for glioblastoma (GBM) therapy resistance and recurrence. Tumor necrosis factor α (TNFα) and TNF signaling pathway display an antitumor activity in preclinical models and in tumor patients. However, TNFα exhibits no significance for glioma clinical prognosis based on the Glioma Genome Atlas database. This study aimed to explore whether TNFα of tumor microenvironment maintains self-renewal of GSCs and promotes worse prognosis in glioma patients.

METHODS

Spatial transcriptomics, immunoblotting, sphere formation assay, extreme limiting dilution, and gene expression analysis were used to determine the role of TNFα on GSC's self-renewal. Mass spectrometry, RNA-sequencing detection, bioinformatic analyses, qRT-RNA, immunofluorescence, immunohistochemistry, single-cell RNA sequencing, in vitro and in vivo models were used to uncover the mechanism of TNFα-induced GSC self-renewal.

RESULTS

A low level of TNFα displays a promoting effect on GSC self-renewal and worse glioma prognosis. Mechanistically, Vasorin (VASN) mediated TNFα-induced self-renewal by potentiating glycolysis. Lactate produced by glycolysis inhibits the TNFα secretion of tumor-associated macrophages (TAMs) and maintains TNFα at a low level.

CONCLUSIONS

TNFα-induced GSC self-renewal mediated by VASN provides a possible explanation for the failures of endogenous TNFα effect on GBM. A combination of targeting VASN and TNFα antitumor effect may be an effective approach for treating GBM.

Quality control for single-cell analysis of high-plex tissue profiles using CyLinter

Tumors are complex assemblies of cellular and acellular structures patterned on spatial scales from microns to centimeters. Study of these assemblies has advanced dramatically with the introduction of high-plex spatial profiling. Image-based profiling methods reveal the intensities and spatial distributions of 20-100 proteins at subcellular resolution in 103-107 cells per specimen. Despite extensive work on methods for extracting single-cell data from these images, all tissue images contain artifacts such as folds, debris, antibody aggregates, optical aberrations and image processing errors that arise from imperfections in specimen preparation, data acquisition, image assembly and feature extraction. Here we show that these artifacts dramatically impact single-cell data analysis, obscuring meaningful biological interpretation. We describe an interactive quality control software tool, CyLinter, that identifies and removes data associated with imaging artifacts. CyLinter greatly improves single-cell analysis, especially for archival specimens sectioned many years before data collection, such as those from clinical trials.

Harnessing luciferase chemistry in regulated cell death modalities and autophagy: overview and perspectives

Regulated cell death is a fate of cells in (patho)physiological conditions during which extrinsic or intrinsic signals or redox equilibrium pathways following infection, cellular stress or injury are coupled to cell death modalities like apoptosis, necroptosis, pyroptosis or ferroptosis. An immediate survival response to cellular stress is often induction of autophagy, a process that deals with removal of aggregated proteins and damaged organelles by a lysosomal recycling process. These cellular processes and their regulation are crucial in several human diseases. Exploiting high-throughput assays which discriminate distinct cell death modalities and autophagy are critical to identify potential therapeutic agents that modulate these cellular responses. In the past few years, luciferase-based assays have been widely developed for assessing regulated cell death and autophagy pathways due to their simplicity, sensitivity, known chemistry, different spectral properties and high-throughput potential. Here, we review basic principles of bioluminescent reactions from a mechanistic perspective, along with their implication in vitro and in vivo for probing cell death and autophagy pathways. These include applying luciferase-, luciferin-, and ATP-based biosensors for investigating regulated cell death modalities. We discuss multiplex bioluminescence platforms which simultaneously distinguish between the various cell death phenomena and cellular stress recovery processes such as autophagy. We also highlight the recent technological achievements of bioluminescent tools for the prediction of drug effectiveness in pathways associated with regulated cell death.

Understanding metabolic plasticity at single cell resolution

It is increasingly clear that cellular metabolic function varies not just between cells of different tissues, but also within tissues and cell types. In this essay, we envision how differences in central carbon metabolism arise from multiple sources, including the cell cycle, circadian rhythms, intrinsic metabolic cycles, and others. We also discuss and compare methods that enable such variation to be detected, including single-cell metabolomics and RNA-sequencing. We pay particular attention to biosensors for AMPK and central carbon metabolites, which when used in combination with metabolic perturbations, provide clear evidence of cellular variance in metabolic function.

Survival Resonances during Fractional Killing of Cell Populations

Fractional killing in response to drugs is a hallmark of nongenetic cellular heterogeneity. Yet how individual lineages evade drug treatment, as observed in bacteria and cancer cells, is not quantitatively understood. We study a stochastic population model with age-dependent division and death rates, allowing for persistence. In periodic drug environments, we discover peaks in the survival probabilities at division or death times that are multiples of the environment duration. The survival resonances are unseen in unstructured populations and are amplified by persistence.

Drug tolerance and persistence in bacteria, fungi and cancer cells: Role of non-genetic heterogeneity

A common feature of bacterial, fungal and cancer cell populations upon treatment is the presence of tolerant and persistent cells able to survive, and sometimes grow, even in the presence of usually inhibitory or lethal drug concentrations, driven by non-genetic differences among individual cells in a population. Here we review and compare data obtained on drug survival in bacteria, fungi and cancer cells to unravel common characteristics and cellular pathways, and to point their singularities. This comparative work also allows to cross-fertilize ideas across fields. We particularly focus on the role of gene expression variability in the emergence of cell-cell non-genetic heterogeneity because it represents a possible common basic molecular process at the origin of most persistence phenomena and could be monitored and tuned to help improve therapeutic interventions.

Cell death: Revisiting the roads to ruin

A paradigm shift in the study of cell death is currently occurring: whereas previously we had always considered that there were "points of no return" in any cell death pathway, we now realize that in many types of active, regulated cell death, this is not the case. We are also learning that cells that "almost die," but nevertheless survive, can transiently take on an altered state, with potential implications for understanding cancer therapies and relapse. In this perspective, we parse the many forms of cell death by analogy to suicide, sabotage, and murder, and consider how cells that might be "instructed" to engage a cell death pathway might nevertheless survive.

Non-genetic differences underlie variability in proliferation among esophageal epithelial clones

Individual cells grown in culture exhibit remarkable differences in their growth, with some cells capable of forming large clusters, while others are limited or fail to grow at all. While these differences have been observed across cell lines and human samples, the growth dynamics and associated cell states remain poorly understood. In this study, we performed clonal tracing through imaging and cellular barcoding of an in vitro model of esophageal epithelial cells (EPC2-hTERT). We found that about 10% of clones grow exponentially, while the remaining have cells that become non-proliferative leading to a halt in the growth rate. Using mathematical models, we demonstrate two distinct growth behaviors: exponential and logistic. Further, we discovered that the propensity to grow exponentially is largely heritable through four doublings and that the less proliferative clones can become highly proliferative through increasing plating density. Combining barcoding with single-cell RNA-sequencing (scRNA-seq), we identified the cellular states associated with the highly proliferative clones, which include genes in the WNT and PI3K pathways. Finally, we identified an enrichment of cells resembling the highly proliferative cell state in the proliferating healthy human esophageal epithelium.

Single-Cell Time-Resolved Metabolomics and Lipidomics Reveal Apoptotic and Ferroptotic Heterogeneity during Foam Cell Formation

Macrophage-derived foam cells play a crucial role in plaque formation and rupture during the progression of atherosclerosis. Traditional studies have often overlooked the heterogeneity of foam cells, focusing instead on populations of cells. To address this, we have developed time-resolved, single-cell metabolomics and lipidomics approaches to explore the heterogeneity of macrophages during foam cell formation. Our dynamic metabolomic and lipidomic analyses revealed a dual regulatory axis involving inflammation and ferroptosis. Further, single-cell metabolomics and lipidomics have delineated a continuum of macrophage states, with varied susceptibilities to apoptosis and ferroptosis. Single-cell transcriptomic profiling confirmed these divergent fates, both in established cell lines and in macrophages derived from peripheral blood monocytes. This research has uncovered the complex molecular interactions that dictate these divergent cell fates, providing crucial insights into the pathogenesis of atherosclerosis.

Unveiling inter-embryo variability in spindle length over time: Towards quantitative phenotype analysis

How can inter-individual variability be quantified? Measuring many features per experiment raises the question of choosing them to recapitulate high-dimensional data. Tackling this challenge on spindle elongation phenotypes, we showed that only three typical elongation patterns describe spindle elongation in C. elegans one-cell embryo. These archetypes, automatically extracted from the experimental data using principal component analysis (PCA), accounted for more than 95% of inter-individual variability of more than 1600 experiments across more than 100 different conditions. The two first archetypes were related to spindle average length and anaphasic elongation rate. The third archetype, accounting for 6% of the variability, was novel and corresponded to a transient spindle shortening in late metaphase, reminiscent of kinetochore function-defect phenotypes. Importantly, these three archetypes were robust to the choice of the dataset and were found even considering only non-treated conditions. Thus, the inter-individual differences between genetically perturbed embryos have the same underlying nature as natural inter-individual differences between wild-type embryos, independently of the temperatures. We thus propose that beyond the apparent complexity of the spindle, only three independent mechanisms account for spindle elongation, weighted differently in the various conditions. Interestingly, the spindle-length archetypes covered both metaphase and anaphase, suggesting that spindle elongation in late metaphase is sufficient to predict the late anaphase length. We validated this idea using a machine-learning approach. Finally, given amounts of these three archetypes could represent a quantitative phenotype. To take advantage of this, we set out to predict interacting genes from a seed based on the PCA coefficients. We exemplified this firstly on the role of tpxl-1 whose homolog tpx2 is involved in spindle microtubule branching, secondly the mechanism regulating metaphase length, and thirdly the central spindle players which set the length at anaphase. We found novel interactors not in public databases but supported by recent experimental publications.

Population Diversity at the Single-Cell Level

Population-scale single-cell genomics is a transformative approach for unraveling the intricate links between genetic and cellular variation. This approach is facilitated by cutting-edge experimental methodologies, including the development of high-throughput single-cell multiomics and advances in multiplexed environmental and genetic perturbations. Examining the effects of natural or synthetic genetic variants across cellular contexts provides insights into the mutual influence of genetics and the environment in shaping cellular heterogeneity. The development of computational methodologies further enables detailed quantitative analysis of molecular variation, offering an opportunity to examine the respective roles of stochastic, intercellular, and interindividual variation. Future opportunities lie in leveraging long-read sequencing, refining disease-relevant cellular models, and embracing predictive and generative machine learning models. These advancements hold the potential for a deeper understanding of the genetic architecture of human molecular traits, which in turn has important implications for understanding the genetic causes of human disease.

Direct observation correlates NFκB cRel in B cells with activating and terminating their proliferative program

Antibody responses require the proliferative expansion of B cells controlled by affinity-dependent signals. Yet, proliferative bursts are heterogeneous, varying between 0 and 8 divisions in response to the same stimulus. NFκB cRel is activated in response to immune stimulation in B cells and is genetically required for proliferation. Here, we asked whether proliferative heterogeneity is controlled by natural variations in cRel abundance. We developed a fluorescent reporter mTFP1-cRel for the direct observation of cRel in live proliferating B cells. We found that cRel is heterogeneously distributed among naïve B cells, which are enriched for high expressors in a heavy-tailed distribution. We found that high cRel expressors show faster activation of the proliferative program, but do not sustain it well, with population expansion decaying earlier. With a mathematical model of the molecular network, we showed that cRel heterogeneity arises from balancing positive feedback by autoregulation and negative feedback by its inhibitor IκBε, confirmed by mouse knockouts. Using live-cell fluorescence microscopy, we showed that increased cRel primes B cells for early proliferation via higher basal expression of the cell cycle driver cMyc. However, peak cMyc induction amplitude is constrained by incoherent feedforward regulation, decoding the fold change of cRel activity to terminate the proliferative burst. This results in a complex nonlinear, nonmonotonic relationship between cRel expression and the extent of proliferation. These findings emphasize the importance of direct observational studies to complement gene knockout results and to learn about quantitative relationships between biological processes and their key regulators in the context of natural variations.

Spatial transcriptomics reveals influence of microenvironment on intrinsic fates in melanoma therapy resistance

Resistance to cancer therapy is driven by both cell-intrinsic and microenvironmental factors. Previous work has revealed that multiple resistant cell fates emerge in melanoma following treatment with targeted therapy and that, in vitro, these resistant fates are determined by the transcriptional state of individual cells prior to exposure to treatment. What remains unclear is whether these resistant fates are shared across different genetic backgrounds and how, if at all, these resistant fates interact with the tumor microenvironment. Through spatial transcriptomics and single-cell RNA sequencing, we uncovered distinct resistance programs in melanoma cells shaped by both intrinsic cellular states and the tumor microenvironment. Consensus non-negative matrix factorization revealed shared intrinsic resistance programs across different cell lines, highlighting the presence of universal and unique resistance pathways. In patient samples, we demonstrated that these resistance programs coexist within individual tumors and associate with diverse immune signatures, suggesting that the tumor microenvironment and distribution of resistant fates are closely connected. Single-cell resolution spatial transcriptomics in xenograft models revealed both intrinsically determined and extrinsically influenced resistant fates. Overall, this work demonstrates that each therapy resistant fate coexists with a distinct immune microenvironment in tumors and that, in vivo, tissue features, such as regions of necrosis, can influence which resistant fate is adopted.

Signal execution modes emerge in biochemical reaction networks calibrated to experimental data

Mathematical models of biomolecular networks are commonly used to study cellular processes; however, their usefulness to explain and predict dynamic behaviors is often questioned due to the unclear relationship between parameter uncertainty and network dynamics. In this work, we introduce PyDyNo (Python dynamic analysis of biochemical networks), a non-equilibrium reaction-flux based analysis to identify dominant reaction paths within a biochemical reaction network calibrated to experimental data. We first show, in a simplified apoptosis execution model, that despite the thousands of parameter vectors with equally good fits to experimental data, our framework identifies the dynamic differences between these parameter sets and outputs three dominant execution modes, which exhibit varying sensitivity to perturbations. We then apply our methodology to JAK2/STAT5 network in colony-forming unit-erythroid (CFU-E) cells and provide previously unrecognized mechanistic explanation for the survival responses of CFU-E cell population that would have been impossible to deduce with traditional protein-concentration based analyses.

A stochastic vs deterministic perspective on the timing of cellular events

Cells are the fundamental units of life, and like all life forms, they change over time. Changes in cell state are driven by molecular processes; of these many are initiated when molecule numbers reach and exceed specific thresholds, a characteristic that can be described as "digital cellular logic". Here we show how molecular and cellular noise profoundly influence the time to cross a critical threshold-the first-passage time-and map out scenarios in which stochastic dynamics result in shorter or longer average first-passage times compared to noise-less dynamics. We illustrate the dependence of the mean first-passage time on noise for a set of exemplar models of gene expression, auto-regulatory feedback control, and enzyme-mediated catalysis. Our theory provides intuitive insight into the origin of these effects and underscores two important insights: (i) deterministic predictions for cellular event timing can be highly inaccurate when molecule numbers are within the range known for many cells; (ii) molecular noise can significantly shift mean first-passage times, particularly within auto-regulatory genetic feedback circuits.

Multiplexed single-cell lineage tracing of mitotic kinesin inhibitor resistance in glioblastoma

Glioblastoma (GBM) is a deadly brain tumor, and the kinesin motor KIF11 is an attractive therapeutic target with roles in proliferation and invasion. Resistance to KIF11 inhibitors, which has mainly been studied in animal models, presents significant challenges. We use lineage-tracing barcodes and single-cell RNA sequencing to analyze resistance in patient-derived GBM neurospheres treated with ispinesib, a potent KIF11 inhibitor. Similar to GBM progression in patients, untreated cells lose their neural lineage identity and become mesenchymal, which is associated with poor prognosis. Conversely, cells subjected to long-term ispinesib treatment exhibit a proneural phenotype. We generate patient-derived xenografts and show that ispinesib-resistant cells form less aggressive tumors in vivo, even in the absence of drug. Moreover, treatment of human ex vivo GBM slices with ispinesib demonstrates phenotypic alignment with in vitro responses, underscoring the clinical relevance of our findings. Finally, using retrospective lineage tracing, we identify drugs that are synergistic with ispinesib.

Effect of genomic and cellular environments on gene expression noise

BACKGROUND

Individual cells from isogenic populations often display large cell-to-cell differences in gene expression. This "noise" in expression derives from several sources, including the genomic and cellular environment in which a gene resides. Large-scale maps of genomic environments have revealed the effects of epigenetic modifications and transcription factor occupancy on mean expression levels, but leveraging such maps to explain expression noise will require new methods to assay how expression noise changes at locations across the genome.

RESULTS

To address this gap, we present Single-cell Analysis of Reporter Gene Expression Noise and Transcriptome (SARGENT), a method that simultaneously measures the noisiness of reporter genes integrated throughout the genome and the global mRNA profiles of individual reporter-gene-containing cells. Using SARGENT, we perform the first comprehensive genome-wide survey of how genomic locations impact gene expression noise. We find that the mean and noise of expression correlate with different histone modifications. We quantify the intrinsic and extrinsic components of reporter gene noise and, using the associated mRNA profiles, assign the extrinsic component to differences between the CD24+ "stem-like" substate and the more "differentiated" substate. SARGENT also reveals the effects of transgene integrations on endogenous gene expression, which will help guide the search for "safe-harbor" loci.

CONCLUSIONS

Taken together, we show that SARGENT is a powerful tool to measure both the mean and noise of gene expression at locations across the genome and that the data generatd by SARGENT reveals important insights into the regulation of gene expression noise genome-wide.

Synthetic protein circuits for programmable control of mammalian cell death

Natural cell death pathways such as apoptosis and pyroptosis play dual roles: they eliminate harmful cells and modulate the immune system by dampening or stimulating inflammation. Synthetic protein circuits capable of triggering specific death programs in target cells could similarly remove harmful cells while appropriately modulating immune responses. However, cells actively influence their death modes in response to natural signals, making it challenging to control death modes. Here, we introduce naturally inspired "synpoptosis" circuits that proteolytically regulate engineered executioner proteins and mammalian cell death. These circuits direct cell death modes, respond to combinations of protease inputs, and selectively eliminate target cells. Furthermore, synpoptosis circuits can be transmitted intercellularly, offering a foundation for engineering synthetic killer cells that induce desired death programs in target cells without self-destruction. Together, these results lay the groundwork for programmable control of mammalian cell death.

SCO-pH: Microfluidic dynamic phenotyping platform for high-throughput screening of single cell acidification

Studies on the dynamics of single cell phenotyping have been hampered by the lack of quantitative high-throughput metabolism assays. Extracellular acidification, a prominent phenotype, yields significant insights into cellular metabolism, including tumorigenicity. Here, we develop a versatile microfluidic system for single cell optical pH analysis (SCO-pH), which compartmentalizes single cells in 140-pL droplets and immobilizes approximately 40,000 droplets in a two-dimensional array for temporal extracellular pH analysis. SCO-pH distinguishes cells undergoing hyperglycolysis induced by oligomycin A from untreated cells by monitoring their extracellular acidification. To facilitate pH sensing in each droplet, we encapsulate a cell-impermeable pH probe whose fluorescence intensities are quantified. Using this approach, we can differentiate hyperglycolytic cells and concurrently observe single cell heterogeneity in extracellular acidification dynamics. This high-throughput system will be useful in applications that require dynamic phenotyping of single cells with significant heterogeneity.

Complementing Cell Taxonomies with a Multicellular Analysis of Tissues

The application of single-cell molecular profiling coupled with spatial technologies has enabled charting of cellular heterogeneity in reference tissues and in disease. This new wave of molecular data has highlighted the expected diversity of single-cell dynamics upon shared external queues and spatial organizations. However, little is known about the relationship between single-cell heterogeneity and the emergence and maintenance of robust multicellular processes in developed tissues and its role in (patho)physiology. Here, we present emerging computational modeling strategies that use increasingly available large-scale cross-condition single-cell and spatial datasets to study multicellular organization in tissues and complement cell taxonomies. This perspective should enable us to better understand how cells within tissues collectively process information and adapt synchronized responses in disease contexts and to bridge the gap between structural changes and functions in tissues.

Clonal differences underlie variable responses to sequential and prolonged treatment

Cancer cells exhibit dramatic differences in gene expression at the single-cell level, which can predict whether they become resistant to treatment. Treatment perpetuates this heterogeneity, resulting in a diversity of cell states among resistant clones. However, it remains unclear whether these differences lead to distinct responses when another treatment is applied or the same treatment is continued. In this study, we combined single-cell RNA sequencing with barcoding to track resistant clones through prolonged and sequential treatments. We found that cells within the same clone have similar gene expression states after multiple rounds of treatment. Moreover, we demonstrated that individual clones have distinct and differing fates, including growth, survival, or death, when subjected to a second treatment or when the first treatment is continued. By identifying gene expression states that predict clone survival, this work provides a foundation for selecting optimal therapies that target the most aggressive resistant clones within a tumor. A record of this paper's transparent peer review process is included in the supplemental information.

TNF-Related Apoptosis-Inducing Ligand: Non-Apoptotic Signalling

TNF-related apoptosis-inducing ligand (TRAIL or Apo2 or TNFSF10) belongs to the TNF superfamily. When bound to its agonistic receptors, TRAIL can induce apoptosis in tumour cells, while sparing healthy cells. Over the last three decades, this tumour selectivity has prompted many studies aiming at evaluating the anti-tumoral potential of TRAIL or its derivatives. Although most of these attempts have failed, so far, novel formulations are still being evaluated. However, emerging evidence indicates that TRAIL can also trigger a non-canonical signal transduction pathway that is likely to be detrimental for its use in oncology. Likewise, an increasing number of studies suggest that in some circumstances TRAIL can induce, via Death receptor 5 (DR5), tumour cell motility, potentially leading to and contributing to tumour metastasis. While the pro-apoptotic signal transduction machinery of TRAIL is well known from a mechanistic point of view, that of the non-canonical pathway is less understood. In this study, we the current state of knowledge of TRAIL non-canonical signalling.

Apoptotic signaling: Beyond cell death

Apoptosis is the best described form of regulated cell death, and was, until relatively recently, considered irreversible once particular biochemical points-of-no-return were activated. In this manuscript, we examine the mechanisms cells use to escape from a self-amplifying death signaling module. We discuss the role of feedback, dynamics, propagation, and noise in apoptotic signaling. We conclude with a revised model for the role of apoptosis in animal development, homeostasis, and disease.

Death and survival from executioner caspase activation

Executioner caspases are evolutionarily conserved regulators of cell death under apoptotic stress. Activated executioner caspases drive apoptotic cell death through cleavage of diverse protein substrates or pyroptotic cell death in the presence of gasdermin E. On the other hand, activation of executioner caspases can also trigger pro-survival and pro-proliferation signals. In recent years, a growing body of studies have demonstrated that cells can survive from executioner caspase activation in response to stress and that the survivors undergo molecular and phenotypic alterations. This review focuses on death and survival from executioner caspase activation, summarizing the role of executioner caspases in apoptotic and pyroptotic cell death and discussing the potential mechanism and consequences of survival from stress-induced executioner caspase activation.

Toward a predictive understanding of epithelial cell death

Epithelial cell death is highly prevalent during development and tissue homeostasis. While we have a rather good understanding of the molecular regulators of programmed cell death, especially for apoptosis, we still fail to predict when, where, how many and which specific cells will die in a tissue. This likely relies on the much more complex picture of apoptosis regulation in a tissular and epithelial context, which entails cell autonomous but also non-cell autonomous factors, diverse feedback and multiple layers of regulation of the commitment to apoptosis. In this review, we illustrate this complexity of epithelial apoptosis regulation by describing these different layers of control, all demonstrating that local cell death probability is a complex emerging feature. We first focus on non-cell autonomous factors that can locally modulate the rate of cell death, including cell competition, mechanical input and geometry as well as systemic effects. We then describe the multiple feedback mechanisms generated by cell death itself. We also outline the multiple layers of regulation of epithelial cell death, including the coordination of extrusion and regulation occurring downstream of effector caspases. Eventually, we propose a roadmap to reach a more predictive understanding of cell death regulation in an epithelial context.

Hsp70-Bim incoherent feedforward loop contributes to cell-fate heterogeneity and fractional killing

BACKGROUND AND PURPOSE

Although chemotherapeutics or molecular targeted drugs often elicit profound initial responses, fractional killing capable of driving acquired resistance can persist. Identifying stress-induced negative feedback or an incoherent feedforward loop (IFFL), which may contribute to fractional killing, is urgently needed.

EXPERIMENTAL APPROACH

Mathematical modelling was used to identify how and to what extent a recently reported Hsp70-Bim protein-protein interaction (PPI) contributes to the adaptation of the Bcl-2 network. Experimental validation was made by using a specific inhibitor of Hsp70-Bim PPI, S1g-2, as chemical tool. Bifurcation analysis and stochastic simulation were used for the theoretical study of the impact of Hsp70-Bim PPI on cell-fate heterogeneity and factional killing.

KEY RESULTS

The Hsp70-Bim-AKT circuit forms an IFFL that greatly contributes to the adaptation of the Bcl-2-regulated apoptosis network, thus leading to fractional killing. This adaptive programme enhances noise-induced cell-fate heterogeneity by shifting from a saddle-node to a saddle-collision transition scenario.

CONCLUSION AND IMPLICATIONS

Hsp70-Bim IFFL serves as a molecular pathway induced by DNA damaging drugs or tyrosine kinase inhibitors that enabled fractional killing, whereby acquired resistance emerges. A synergistic strategy is unveiled for overcoming fractional killing by suppressing Hsp70-Bim PPI.

Increased heterogeneity in expression of genes associated with cancer progression and drug resistance

Fluctuations in the number of regulatory molecules and differences in timings of molecular events can generate variation in gene expression among genetically identical cells in the same environmental condition. This variation, termed as expression noise, can create differences in metabolic state and cellular functions, leading to phenotypic heterogeneity. Expression noise and phenotypic heterogeneity have been recognized as important contributors to intra-tumor heterogeneity, and have been associated with cancer growth, progression, and therapy resistance. However, how expression noise changes with cancer progression in actual cancer patients has remained poorly explored. Such an analysis, through identification of genes with increasing expression noise, can provide valuable insights into generation of intra-tumor heterogeneity, and could have important implications for understanding immune-suppression, drug tolerance and therapy resistance. In this work, we performed a genome-wide identification of changes in gene expression noise with cancer progression using single-cell RNA-seq data of lung adenocarcinoma patients at different stages of cancer. We identified 37 genes in epithelial cells that showed an increasing noise trend with cancer progression, many of which were also associated with cancer growth, EMT and therapy resistance. We found that expression of several of these genes was positively associated with expression of mitochondrial genes, suggesting an important role of mitochondria in generation of heterogeneity. In addition, we uncovered substantial differences in sample-specific noise profiles which could have implications for personalized prognosis and treatment.

Functional genomics in inborn errors of immunity

Inborn errors of immunity (IEI) comprise a diverse spectrum of 485 disorders as recognized by the International Union of Immunological Societies Committee on Inborn Error of Immunity in 2022. While IEI are monogenic by definition, they illuminate various pathways involved in the pathogenesis of polygenic immune dysregulation as in autoimmune or autoinflammatory syndromes, or in more common infectious diseases that may not have a significant genetic basis. Rapid improvement in genomic technologies has been the main driver of the accelerated rate of discovery of IEI and has led to the development of innovative treatment strategies. In this review, we will explore various facets of IEI, delving into the distinctions between PIDD and PIRD. We will examine how Mendelian inheritance patterns contribute to these disorders and discuss advancements in functional genomics that aid in characterizing new IEI. Additionally, we will explore how emerging genomic tools help to characterize new IEI as well as how they are paving the way for innovative treatment approaches for managing and potentially curing these complex immune conditions.

The population context is a driver of the heterogeneous response of epithelial cells to interferons

Isogenic cells respond in a heterogeneous manner to interferon. Using a micropatterning approach combined with high-content imaging and spatial analyses, we characterized how the population context (position of a cell with respect to neighboring cells) of epithelial cells affects their response to interferons. We identified that cells at the edge of cellular colonies are more responsive than cells embedded within colonies. We determined that this spatial heterogeneity in interferon response resulted from the polarized basolateral interferon receptor distribution, making cells located in the center of cellular colonies less responsive to ectopic interferon stimulation. This was conserved across cell lines and primary cells originating from epithelial tissues. Importantly, cells embedded within cellular colonies were not protected from viral infection by apical interferon treatment, demonstrating that the population context-driven heterogeneous response to interferon influences the outcome of viral infection. Our data highlights that the behavior of isolated cells does not directly translate to their behavior in a population, placing the population context as one important factor influencing heterogeneity during interferon response in epithelial cells.

Cell cycle plasticity underlies fractional resistance to palbociclib in ER+/HER2- breast tumor cells

The CDK4/6 inhibitor palbociclib blocks cell cycle progression in Estrogen receptor-positive, human epidermal growth factor 2 receptor-negative (ER+/HER2-) breast tumor cells. Despite the drug's success in improving patient outcomes, a small percentage of tumor cells continues to divide in the presence of palbociclib-a phenomenon we refer to as fractional resistance. It is critical to understand the cellular mechanisms underlying fractional resistance because the precise percentage of resistant cells in patient tissue is a strong predictor of clinical outcomes. Here, we hypothesize that fractional resistance arises from cell-to-cell differences in core cell cycle regulators that allow a subset of cells to escape CDK4/6 inhibitor therapy. We used multiplex, single-cell imaging to identify fractionally resistant cells in both cultured and primary breast tumor samples resected from patients. Resistant cells showed premature accumulation of multiple G1 regulators including E2F1, retinoblastoma protein, and CDK2, as well as enhanced sensitivity to pharmacological inhibition of CDK2 activity. Using trajectory inference approaches, we show how plasticity among cell cycle regulators gives rise to alternate cell cycle "paths" that allow individual tumor cells to escape palbociclib treatment. Understanding drivers of cell cycle plasticity, and how to eliminate resistant cell cycle paths, could lead to improved cancer therapies targeting fractionally resistant cells to improve patient outcomes.

Inertial Multi-Force Deformability Cytometry for High-Throughput, High-Accuracy, and High-Applicability Tumor Cell Mechanotyping

Previous on-chip technologies for characterizing the cellular mechanical properties often suffer from a low throughput and limited sensitivity. Herein, an inertial multi-force deformability cytometry (IMFDC) is developed for high-throughput, high-accuracy, and high-applicability tumor cell mechanotyping. Three different deformations, including shear deformations and stretch deformations under different forces, are integrated with the IMFDC. The 3D inertial focusing of cells enables the cells to deform by an identical fluid flow, and 10 parameters, such as cell area, perimeter, deformability, roundness, and rectangle deformability, are obtained in three deformations. The IMFDC is able to evaluate the deformability of different cells that are sensitive to different forces on a single chip, demonstrating the high applicability of the IMFDC in analyzing different cell lines. In identifying cell types, the three deformations exhibit different mechanical responses to cells with different sizes and deformability. A discrimination accuracy of ≈93% for both MDA-MB-231 and MCF-10A cells and a throughput of ≈500 cells s-1 can be achieved using the multiple-parameters-based machine learning model. Finally, the mechanical properties of metastatic tumor cells in pleural and peritoneal effusions are characterized, enabling the practical application of the IMFDC in clinical cancer diagnosis.

The ability to sense the environment is heterogeneously distributed in cell populations

Channel capacity of signaling networks quantifies their fidelity in sensing extracellular inputs. Low estimates of channel capacities for several mammalian signaling networks suggest that cells can barely detect the presence/absence of environmental signals. However, given the extensive heterogeneity and temporal stability of cell state variables, we hypothesize that the sensing ability itself may depend on the state of the cells. In this work, we present an information-theoretic framework to quantify the distribution of sensing abilities from single-cell data. Using data on two mammalian pathways, we show that sensing abilities are widely distributed in the population and most cells achieve better resolution of inputs compared to an 'average cell'. We verify these predictions using live-cell imaging data on the IGFR/FoxO pathway. Importantly, we identify cell state variables that correlate with cells' sensing abilities. This information-theoretic framework will significantly improve our understanding of how cells sense in their environment.

A microfluidic array enabling generation of identical biochemical stimulating signals to trapped biological cells for single-cell dynamics

Microfluidic-based analyses of single-cell dynamics in response to dynamic biochemical signals are emerging as pivotal approaches for investigating the effects of extracellular microenvironmental biochemical factors on cellular structure, function, and behavior. However, current devices often fail to consistently apply identical dynamic biochemical signals to trapped cells. In this study, we introduce a novel radially distributed single-cell trapping microfluidic array, designed to quantitatively and consistently apply identical biochemical stimulating signals to each trapped cell. Numerical simulations were employed to optimize microchannel geometry, enhancing trapping efficiency while minimizing signal distortion. Experimental validation demonstrated the trapping success rate and the single-cell trapping efficiency exceeding 99% and 85%, respectively. The microarray's capability to deliver identical dynamic biochemical stimulating signals, with various waveforms, to each unit was confirmed through fluorescein transport tests. Furthermore, we examined the intracellular calcium dynamics of U-2 OS human osteosarcoma cells in response to dynamic ATP signals, observing both single-peak calcium responses and calcium oscillations, which were modelled by a second-order system with a natural frequency of 1.6 mHz. Overall, our proposed microfluidic array offers a robust and valuable framework for advancing the understanding of single-cell dynamics.

Measuring Drug Response with Single-Cell Growth Rate Quantification

Intratumoral heterogeneity is a substantial cause of drug resistance development during chemotherapy or other drug treatments for cancer. Therefore, monitoring and measuring cell exposure and response to drugs at the single-cell level are crucial. Previous research suggested that the single-cell growth rate can be used to investigate drug-cell interactions. However, currently established methods for quantifying single-cell growth are limited to isolated or monolayer cells. Here, we introduce a technique that accurately measures both 2D and 3D cell growth rates using label-free ratiometric stimulated Raman scattering (SRS) microscopy. We use deuterated amino acids, leucine, isoleucine, and valine, as tracers and measure the C-D SRS signal from deuterium-labeled proteins and the C-H SRS signal from unlabeled proteins simultaneously to determine the cell growth rate at the single-cell level. The technique offers single-cell level drug sensitivity measurement with a shorter turnaround time (within 12 h) than most traditional assays. The submicrometer resolution of the imaging technique allows us to examine the effects of chemotherapeutic drugs, including kinase inhibitors, mitotic inhibitors, and topoisomerase II inhibitors, on both the cell growth rate and morphology. The capability of quantifying 3D cell growth rates provides insight into a deeper understanding of the cell-drug interaction in the actual tumor environment.

Bifunctional Small Molecules That Induce Nuclear Localization and Targeted Transcriptional Regulation

The aberrant localization of proteins in cells is a key factor in the development of various diseases, including cancer and neurodegenerative disease. To better understand and potentially manipulate protein localization for therapeutic purposes, we engineered bifunctional compounds that bind to proteins in separate cellular compartments. We show these compounds induce nuclear import of cytosolic cargoes, using nuclear-localized BRD4 as a "carrier" for co-import and nuclear trapping of cytosolic proteins. We use this system to calculate kinetic constants for passive diffusion across the nuclear pore and demonstrate single-cell heterogeneity in response to these bifunctional molecules with cells requiring high carrier to cargo expression for complete import. We also observe incorporation of cargo into BRD4-containing condensates. Proteins shown to be substrates for nuclear transport include oncogenic mutant nucleophosmin (NPM1c) and mutant PI3K catalytic subunit alpha (PIK3CAE545K), suggesting potential applications to cancer treatment. In addition, we demonstrate that chemically induced localization of BRD4 to cytosolic-localized DNA-binding proteins, namely, IRF1 with a nuclear export signal, induces target gene expression. These results suggest that induced localization of proteins with bifunctional molecules enables the rewiring of cell circuitry, with significant implications for disease therapy.

Gene transcription regulation by ER at the single cell and allele level

In this short review we discuss the current view of how the estrogen receptor (ER), a pivotal member of the nuclear receptor superfamily of transcription factors, regulates gene transcription at the single cell and allele level, focusing on in vitro cell line models. We discuss central topics and new trends in molecular biology including phenotypic heterogeneity, single cell sequencing, nuclear phase separated condensates, single cell imaging, and image analysis methods, with particular focus on the methodologies and results that have been reported in the last few years using microscopy-based techniques. These observations augment the results from biochemical assays that lead to a much more complex and dynamic view of how ER, and arguably most transcription factors, act to regulate gene transcription.

Modulating the dynamics of NFκB and PI3K enhances the ensemble-level TNFR1 signaling mediated apoptotic response

Cell-to-cell variability during TNFα stimulated Tumor Necrosis Factor Receptor 1 (TNFR1) signaling can lead to single-cell level pro-survival and apoptotic responses. This variability stems from the heterogeneity in signal flow through intracellular signaling entities that regulate the balance between these two phenotypes. Using systematic Boolean dynamic modeling of a TNFR1 signaling network, we demonstrate that the signal flow path variability can be modulated to enable cells favour apoptosis. We developed a computationally efficient approach "Boolean Modeling based Prediction of Steady-state probability of Phenotype Reachability (BM-ProSPR)" to accurately predict the network's ability to settle into different phenotypes. Model analysis juxtaposed with the experimental observations revealed that NFκB and PI3K transient responses guide the XIAP behaviour to coordinate the crucial dynamic cross-talk between the pro-survival and apoptotic arms at the single-cell level. Model predicted the experimental observations that ~31% apoptosis increase can be achieved by arresting Comp1 - IKK* activity which regulates the NFκB and PI3K dynamics. Arresting Comp1 - IKK* activity causes signal flow path re-wiring towards apoptosis without significantly compromising NFκB levels, which govern adequate cell survival. Priming an ensemble of cancerous cells with inhibitors targeting the specific interaction involving Comp1 and IKK* prior to TNFα exposure could enable driving them towards apoptosis.

The ability to sense the environment is heterogeneously distributed in cell populations

Channel capacity of signaling networks quantifies their fidelity in sensing extracellular inputs. Low estimates of channel capacities for several mammalian signaling networks suggest that cells can barely detect the presence/absence of environmental signals. However, given the extensive heterogeneity and temporal stability of cell state variables, we hypothesize that the sensing ability itself may depend on the state of the cells. In this work, we present an information theoretic framework to quantify the distribution of sensing abilities from single cell data. Using data on two mammalian pathways, we show that sensing abilities are widely distributed in the population and most cells achieve better resolution of inputs compared to an " average cell ". We verify these predictions using live cell imaging data on the IGFR/FoxO pathway. Importantly, we identify cell state variables that correlate with cells' sensing abilities. This information theoretic framework will significantly improve our understanding of how cells sense in their environment.

High throughput microscopy and single cell phenotypic image-based analysis in toxicology and drug discovery

Measuring single cell responses to the universe of chemicals (drugs, natural products, environmental toxicants etc.) is of paramount importance to human health as phenotypic variability in sensing stimuli is a hallmark of biology that is considered during high throughput screening. One of the ways to approach this problem is via high throughput, microscopy-based assays coupled with multi-dimensional single cell analysis methods. Here, we will summarize some of the efforts in this vast and growing field, focusing on phenotypic screens (e.g., Cell Painting), single cell analytics and quality control, with particular attention to environmental toxicology and drug screening. We will discuss advantages and limitations of high throughput assays with various end points and levels of complexity.

A Review on Caspases: Key Regulators of Biological Activities and Apoptosis

Caspases are proteolytic enzymes that belong to the cysteine protease family and play a crucial role in homeostasis and programmed cell death. Caspases have been broadly classified by their known roles in apoptosis (caspase-3, caspase-6, caspase-7, caspase-8, and caspase-9 in mammals) and in inflammation (caspase-1, caspase-4, caspase-5, and caspase-12 in humans, and caspase-1, caspase-11, and caspase-12 in mice). Caspases involved in apoptosis have been subclassified by their mechanism of action as either initiator caspases (caspase-8 and caspase-9) or executioner caspases (caspase-3, caspase-6, and caspase-7). Caspases that participate in apoptosis are inhibited by proteins known as inhibitors of apoptosis (IAPs). In addition to apoptosis, caspases play a role in necroptosis, pyroptosis, and autophagy, which are non-apoptotic cell death processes. Dysregulation of caspases features prominently in many human diseases, including cancer, autoimmunity, and neurodegenerative disorders, and increasing evidence shows that altering caspase activity can confer therapeutic benefits. This review covers the different types of caspases, their functions, and their physiological and biological activities and roles in different organisms.

Multiplexed single-cell lineage tracing of mitotic kinesin inhibitor resistance in glioblastoma

Glioblastoma (GBM) is a deadly brain tumor, and the kinesin motor KIF11 is an attractive therapeutic target because of its dual roles in proliferation and invasion. The clinical utility of KIF11 inhibitors has been limited by drug resistance, which has mainly been studied in animal models. We used multiplexed lineage tracing barcodes and scRNA-seq to analyze drug resistance time courses for patient-derived GBM neurospheres treated with ispinesib, a potent KIF11 inhibitor. Similar to GBM progression in patients, untreated cells lost their neural lineage identity and transitioned to a mesenchymal phenotype, which is associated with poor prognosis. In contrast, cells subjected to long-term ispinesib treatment exhibited a proneural phenotype. We generated patient-derived xenografts to show that ispinesib-resistant cells form less aggressive tumors in vivo, even in the absence of drug. Finally, we used lineage barcodes to nominate drug combination targets by retrospective analysis of ispinesib-resistant clones in the drug-naïve setting and identified drugs that are synergistic with ispinesib.

Quantifying cancer cell plasticity with gene regulatory networks and single-cell dynamics

Phenotypic plasticity of cancer cells can lead to complex cell state dynamics during tumor progression and acquired resistance. Highly plastic stem-like states may be inherently drug-resistant. Moreover, cell state dynamics in response to therapy allow a tumor to evade treatment. In both scenarios, quantifying plasticity is essential for identifying high-plasticity states or elucidating transition paths between states. Currently, methods to quantify plasticity tend to focus on 1) quantification of quasi-potential based on the underlying gene regulatory network dynamics of the system; or 2) inference of cell potency based on trajectory inference or lineage tracing in single-cell dynamics. Here, we explore both of these approaches and associated computational tools. We then discuss implications of each approach to plasticity metrics, and relevance to cancer treatment strategies.

CDH1 overexpression sensitizes TRAIL resistant breast cancer cells towards rhTRAIL induced apoptosis

PURPOSE

Tumor necrosis factor (TNF)-related apoptosis-inducing ligand (TRAIL) is well known for its unique ability to induce apoptosis in cancer cells but not normal cells. However, a subpopulation of cancer cells exist that does not respond to toxic doses of TRAIL. In this study, we aimed to identify key factors regulating TRAIL resistance in breast cancer.

METHODS

rhTRAIL (recombinant human TRAIL) resistant cells (TR) isolated from TRAIL sensitive MDA-MB-231 parental cells (TS) were confirmed using trypan blue assay, cell viability assay and AO/EtBr (acridine orange/ethidium bromide) staining. Microarray was performed followed by analysis using DAVID and Cytoscape bioinformatics software to identify the candidate hub gene. Gene expression of the candidate gene was confirmed using real-time PCR and western blot. Candidate gene was overexpressed via transient transfection to identify its significance in the context of rhTRAIL. Breast cancer patient data was obtained from The Cancer Genome Atlas (TCGA) database.

RESULTS

Whole transcriptome analysis identified 4907 differentially expressed genes (DEGs) between TS and TR cells. CDH1 was identified as the candidate hub gene, with 18-degree centrality. We further observed CDH1 protein to be downregulated, overexpression of which increased apoptosis in TR cells after rhTRAIL treatment. TCGA patient data analysis also showed CDH1 mRNA to be low in TRAIL resistant patient group compared to TRAIL sensitive group.

CONCLUSION

CDH1 overexpression sensitizes TR cells towards rhTRAIL induced apoptosis. Therefore, we can hypothesize that CDH1 expression should be taken into account while performing TRAIL therapy in breast cancer.